MD Pierre Bouletreau
email: pierre.bouletreau@chu-lyon.fr

DDS Masrour Makaremi

Ibrahim Badr

FRCSC Md

MD Aurélien Louvrier
email: au.louvrier@gmail.com

MD Nicolas Sigaux
email: nicolas.sigaux@chu-lyon.fr

Pierre Pr

Bouletreau

Artificial Intelligence : Applications in Orthognathic Surgery

Keywords: Artificial Intelligence, Deep Learning, Orthognathic Surgery, Orthodontics

Artificial Intelligence (AI) applications have already invaded our everyday life, and the last 10 years have seen the emergence of very promising applications in the field of medicine.

However, the literature dealing with the potential applications of IA in Orthognathic Surgery is remarkably poor to date. Yet, it is very likely that due to its amazing power in image recognition AI will find tremendous applications in dentofacial deformities recognition in a near future. In this article, we point out the state-of-the-art AI applications in medicine and its potential applications in the field of orthognathic surgery. AI is a very powerful tool and it is the responsibility of the entire medical profession to achieve a positive symbiosis between clinical sense and AI.

Introduction

Artificial Intelligence (AI) is commonly defined as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation". [START_REF] Kaplan | Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence[END_REF] The dramatic increase in computer power over the past 50 years linked to the big data invasion pushed AI applications towards new frontiers. AI is already ubiquitous in our everyday life through our smartphones and cars. It has now gone on to invade the field of medicine.

The last five years have shed some light on very promising applications of AI in radiology, dermatology and oncology. Herein, we present the present and potential future applications of AI in the field of orthognathic surgery.

History

Based on Alan Turing's research work on machine intelligence, the field of AI research officially emerged in the stream of the so-called "Dartmouth Conference" in 1956. After 20 years of initial excitement, the technical limitations of AI programming led to its "first winter": a period marked by massive disinterest of financial investors in this field. In the United Kingdom, the 1973 "Lighthill report" severely criticized the failure of AI projects to achieve their planned goals. In the United States (US), the Mansfield Amendments published in 1969 and 1973 demanded that research funded by the Department of Defense should have direct applications in the US army. AI was considered a technique without any scientific or economic potential and hence lost its funding from the Defense budget for almost 6 years.

At the beginning of the 80's, a new form of AI emerged called "expert systems". This AI was highly promoted by the Japanese government. In that decade, AI research benefited from a new boost as funding worldwide increased once again. This period was marked by some key developments for training neural networks such as backpropagation. However, the market grew progressively disillusioned as this technology did not yield any major economic breakthrough. By the end of the 80's, investor funding once again collapsed leading to the "second winter" of AI. However, research in the field of AI remained active.

In the mid 90's AI finally reached some of its oldest goals. One of its most emblematic milestone was the IBM DeepBlue project which led to the defeat of the world chess champion Gary Kasparov in 1997. This was in part due to increasing computing power.

In the first decade of the XXIst century, AI applications spread rapidly in various fields such as speech recognition, autonomous driving, and medical applications. More recently, the continuous increase in computing power coupled to the availability of very large databases (Big data) fueled AI applications to unprecedented levels. Machine learning techniques that had been developed decades ago, such as convolutional neural networks, finally found applications in a wide variety of domains. A notable milestone for deep learning applications was the Google-Alphago project that allowed an AIpowered machine to beat the world Go champion in 2017. Nowadays, AI research involves various fields of expertise such as mathematics, computer sciences, and cognitive science amongst others.

Applications in Medicine

In the past decade, numerous very promising applications of AI have emerged in Medicine 2 .

Convolutional Neural Networks trained with massive labeled datasets proved to be very efficient in image recognition. The main reason explaining the actual superiority of AI over human doctors in select sub-specialties of medicine is that machines can be educated with hundreds of thousands of clinical cases. This exceeds by far the clinical experience of even the best specialists in any field.

Meanwhile, healthcare data has become more abundant and more complex for each patient. And, the ideal of personalized medicine requires taking into account massive sets of data for each individual patient.

The fear that machines could potentially replace human doctors in a near future is to be taken seriously as the momentum of change in the field of AI research is unprecedented. As such, predicting this evolution is becoming increasingly difficult. However, most experts assume that humans will have to cooperate with a so-called "weak AI" for at least a few more decades. Hence for the time being, it is reasonable to consider AI as a tool that can definitely assist human doctors in various areas such as diagnostic medicine, clinical decision making, and personalized medicine.

The main actors in AI research have entered the global market of connected health many years ago. GAFAM (Google, Amazon, Facebook, Apple, and Microsoft) and their Chinese counterpart BATX (Baidu, Alibaba, Tencent, and Xiaomi) have already massively invested in the field of connected health. [START_REF] Lee | AI Superpowers : China, Silicon Valley, and the New World Order[END_REF] For example, Google's Verily and Deepmind Health already propose AI-powered applications "supporting doctors and nurses to deliver faster, better care to patients" 4 . The massive datasets obtained from these projects (demographic information and clinical data) help in return educate machine learning algorithms. Ultimately, this makes the programs more and more efficient and accurate. Thanks to the huge amount of data analyzed, it is likely that in a near future, AI-powered clinical studies will be statistically more powerful and accurate than "classic" clinical studiesincluding double-blind randomized controlled trials.

Nowadays, medical applications of AI include -but are not limited to -Radiology 5 , Dermatology 6 , Neurology 7 , Ophthalmology 8 , Oncology 9 , Cardiology 10 , Genetics 11 , Emergency Medicine [START_REF] Ghassemi | State of the art review: the data revolution in critical care[END_REF] , and Drug design [START_REF] Zhong | Artificial intelligence in drug design[END_REF] .

Applications in Orthognathic Surgery

To date, and to the best of our knowledge, the literature related to the applications of AI in Orthognathic Surgery is very poor. The only published study related to this topic aimed at applying AI to assess the impact of orthognathic treatment on facial attractiveness and estimated age [START_REF] Patcas | Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age[END_REF] . Applying a computational algorithm based on a Convolutional Neural Network, Patcas et al. showed that most patients' appearance and attractiveness improved with orthognathic treatment. These findings are in line with most "classic" clinical studies. This paper represents a proof of concept that AI must be considered a useful tool in evaluating facial alterations after orthognathic surgery.

Furthermore, the use of AI seems particularly well adapted in the context of orthognathic surgery protocols. The impact it could have in the future is quite consequential.

Several different factors support this rationale:

-Colossal investments are being made by the industry in research and development of AI applied to digital orthodontics and robotic surgery. Large economic returns are expected from these fields (an estimated 3,6 billion € in the field of orthodontics alone).

-The worldwide development of substantial image databases is a proof of the increasing use of digital imagery and photographs in the management of orthognathic cases. Specifically, the introduction of intra oral scanners has radically altered the place of digital imaging in orthodontics and orthognathic surgery.

-Real life demands from practitioners: reconstruction, tridimensional morphometrics, automated treatment planning, customized surgical set up planning, etc... At the outset, it is important to identify the type of AI that is being discussed in this instance.

A high level of AI in medicine is characterized by an autonomous machine that is capable of evaluating different solutions based on a predictable pathologic evolution. This solution is not yet possible in surgical orthodontic protocols nor in general orthodontics.

A weak level of AI is an artificial intelligence that is « non-discernable » and can only concentrate on one specific task. In orthognathic surgery, digital solutions are introduced strictly for the analysis of digital models of dental arches and radiographic imagery.

The impact of digital solutions on surgical-orthodontic protocols can be grouped in the following 4 domains (Fig. 1).

-Improved diagnostic precision using AI enhanced maxillofacial imagery.

-Treatment planning using 3D models.

-CAD/CAM (Computer Aid Design, Computer Aid Manufacturing) manufacture of custom orthodontic and surgical appliances and equipment.

-Improved therapeutic follow-up due to finer interval comparison of results using image superimposing.

Maxillofacial Imagery

AI intervenes at different levels to optimize the acquisition and processing of data, as well as preanalysis of maxillofacial imagery. The various levels of resolution that can be analyzed offers the practitioner a more transparent and global view of the patient's physical characteristics. This is input is essential for determining the correct therapeutic option.

Acquisition.

The use of AI in intra-oral scanner software allows for a quicker and more efficient acquisition.

Moreover, the use of AI in tridimensional radiology optimizes the signal to noise ratio and yields higher quality images using lower doses of radiation. [START_REF] Sun | Digital radiography image denoising using a generative adversarial network[END_REF] .

Interpretation

The techniques employed in machine learning allow for 3D reconstructions that are rapidly increasing in efficiency. They allow for superimposing of various diagnostic tools (ie. CBCT, digital photography, and intra-oral scanners) [START_REF] Cevidanes | Image analysis and superimposition of 3-dimensional cone-beam computed tomography models[END_REF] to identify and calculate the dimensions of the upper airways. This data is particularly pertinent in the treatment of obstructive sleep apnea syndromes [START_REF] Kamaruddin | Comparison of automatic airway analysis function of Invivo5 and Romexis software[END_REF] .

Moreover, the analysis of a lateral cephalogram is quite unrewarding for practitioners. However, it is essential in an orthodontic surgery protocol. Various software exist that incorporate artificial intelligence and machine learning [START_REF] Nishimoto | Personal Computer-Based Cephalometric Landmark Detection With Deep Learning, Using Cephalograms on the Internet[END_REF] technologies to automate cephalometric analyses (Fig. 2). This frees up cognitive resources for the practitioner. Moreover, AI's benefit extends well beyond its 2D analyses capacity. In fact, it enables the practitioner to achieve a true and objective tridimensional perception of a given patient's dento-facial characteristics. It could therefore be a dramatically powerful diagnostic tool. Studies show that a minimum of 100-200 craniometric points are necessary for a biometric analysis of a 3D Cone Beam image of the face [START_REF] Zamora | A study on the reproducibility of cephalometric landmarks when undertaking a three-dimensional (3D) cephalometric analysis[END_REF] . The need for artificial intelligence lies in its capacity to succinctly analyze and interpret so many parameters at once. [START_REF] Faure | 3D cephalometry and artificial intelligence[END_REF] Currently, ambitious AI solutions are being developed with the aim of producing a literary synthesis from biometric data. For example, an AI would diagnose a « right hemifacial excess » when there is a simultaneous right antero-posterior excess, a right vertical excess, associated with a left mandibular deviation.

Treatment Planning

The surgical orthodontic planning presents several different challenges. It is the result of the symbiosis between the surgeon and the orthodontist and cannot be purely instinctive. It must be carefully discussed and verbalized in an ongoing dialogue within the team.

-The planning must take into account the orthodontic phase, which is a long-term process, as opposed to the short-term process of surgery.

-The planning should also take into account the numerous interdependent variables: bony structures, occlusion, periodontal health, orofacial functions (swallowing, breathing, etc.), and facial aesthetics.

It is worth noting that while some of these parameters can be quantified others need a more subjective evaluation.

These paradigms show the need for 3D digital treatment planning software. This can be achieved using a dynamic virtual set up (Clin Check, Insignia, Orthoanalyser, etc...). These software are enhanced by machine learning [START_REF] Hennessy | Clear Aligners Generations and Orthodontic Tooth Movement[END_REF] . Algorithms have replaced the long and fastidious task of setting up plaster stone models and lateral cephalograms.

The dynamic virtual set up are a wonderful dialogue tool between the maxillofacial surgeon and the orthodontist. It allows the practitioners to visualize diagrams of each therapeutic objective and the role of each intervention on the overall result. It is also a very effective tool for dialogue and planning where there is a multidisciplinary approach involved in the orthognathic surgery protocols (ENT, general dentist, sleep specialist) as these specialists may not be as familiar with the technicalities of orthodontic surgery protocols. Virtual set up software are also great tools to discuss and explain the procedures to the patient in order to improve their understanding and implication in the orthognathic surgical protocol. [START_REF] Pagani | The Use of Invisalign® System in the Management of the Orthodontic Treatment before and after Class III Surgical Approach[END_REF]

Custom Orthodontic and Surgical Appliances

Following the planning phase, the next step using the CAD/CAM technology allows the custom manufacturing of medical devices. Indeed, 3D printing contributed to the digital revolution that has allowed innovative and invisible treatments (clear aligner treatments, or lingual therapy) [START_REF] Lagravère | The treatment effects of Invisalign orthodontic aligners: a systematic review[END_REF] . It also allows for creating appliances that incorporate the biomechanics of alveolar decompression (Fig. 3).

In the field of orthognathic surgery, CAD/CAM technology found recent applications in treatment planning and transfer methods.

Standard surgical planning may be achieved by cephalometric and clinical planning. In addition, Virtual Surgical Planning (VSP) has been developed as an extension to these standard methods and is now accepted as a very powerful tool to assist the surgeon in most difficult cases such as facial asymmetries [START_REF] Steinhuber | Is Virtual Surgical Planning in Orthognathic Surgery Faster Than Conventional Planning? A Time and Workflow Analysis of an Office-Based Workflow for Single-and Double-Jaw Surgery[END_REF] . Moreover, the dematerialized medical data it generates makes remote collaboration between practitioners more fluid.

As far as transfer (ie: clinical application of the planning) is concerned, CAD/CAM technology brought about significant progress in the last decade. Currently available transfer technologies include CAD/CAM surgical splints, CAD/CAM splints with extra-oral bone support, patient-specific osteosynthesis titanium miniplates, and surgical navigation. [START_REF] Pascal | Current status of surgical planning and transfer methods in orthognathic surgery[END_REF] (Fig. 4)

Treatment follow-up

Visualizing and quantifying the impact of a given treatment on different anatomical structures presents several advantages in orthognathic surgery. It allows the practitioner to validate the chosen optimal treatment plan and evaluate the pros and cons of alternative treatment options. Additionally, it offers the opportunity for patients to visualize the effects of the treatment hence optimizing informed consent. In addition, it enhances the work of the orthodontist and maxillofacial surgeon by objectively presenting the impact of the different phases of the treatment through a non-biased filter.

Moreover, the use of AI would allow the practitioner to be better equipped to tackle future clinical challenges with similar presentations. AI is particularly well adapted as a tool for treatment follow up as it allows for the superposition of various digital images.

For example, software that was designed with different types of machine learning available for use in the same intra oral scanner [START_REF] Viñas | Superimposition of 3D digital models: A case report[END_REF][START_REF] Ganzer | A novel method for superimposition and measurements on maxillary digital 3D models-studies on validity and reliability[END_REF] allows for the superimposition and visualization of dental arch movements of two digital models in a single patient side-by-side. [START_REF] Viñas | Superimposition of 3D digital models: A case report[END_REF][START_REF] Ganzer | A novel method for superimposition and measurements on maxillary digital 3D models-studies on validity and reliability[END_REF] (Fig. 5) Other AI based solutions offer the ability to combine digital photography and 3 D models called « 3D Matching » to yield a 3D image of the dental arch models using the actual teeth photographs. This model is dynamically adjusted based on the evolution of the treatment over time. [START_REF] Roisin | Contrôle à distance en orthodontie : fondements et description de la solution Dental Monitoring TM[END_REF] (Fig. 6) Naturally, in orthognathic surgery, the ability to superimpose x ray imaging is particularly useful. [START_REF] Cevidanes | Superimposition of 3D cone-beam CT models of orthognathic surgery patients[END_REF] Thanks to the abilities of artificial intelligence in 3D radiology, it is possible to automate the use of geometric morphometric tools such as Procuste methods without a burdensome and labor intensive intervention from a practicionner. The generated images can be Procuste superimpositions entirely [START_REF] Oueiss | Les rapports tridimensionnels de la base du crâne et du massif maxillo-facial : intérêts en orthodontie et anthropobiologie[END_REF] , or limited to a designated area that is considered stable [START_REF] Oueiss | Étude des asymétries dans les grandes dysmorphies antéro-postérieures[END_REF] . Notably in mandibular advancement surgery, a superimposition of a « designated area » comprised of the sagittal ramus posterior to the osteotomy is of great importance.

Discussion

The extraordinarily fast pace of progresses in the field of computer technology during the last 20 years, following Moore's law, makes it very difficult to predict the path that AI intrusion in the medical field will follow in the future. However, as far as the current applications of AI in medicine are concerned, it seems reasonable to hypothesize that physicians and AI will closely cooperate in a near future. Regarding orthognathic surgery specifically, it is worth contemplating how AI could enhance human capacities in elaborating a treatment plan and what its limitations are in this specific task.

Elaborating a treatment plan in orthognathic surgery is based on three main components. The first component is a basic knowledge in the fields of craniofacial anatomy, orofacial functions, dysmorphisms and their etiologies, as well as surgical techniques. This component is acquired through textbook knowledge and personal experience.

The second component is linked to the artistic sense of each physician. Indeed, facial aesthetics is a major feature of orthognathic surgery, and elaborating a treatment plan requires a certain artistic sensitivity on the surgeon's behalf. The latter must be able to perform an accurate facial esthetic examination. Furthermore, the surgeon must be able to predict the esthetic impact of the skeletal displacements of the maxilla and mandible necessary to correct the dental malocclusion. To this effect, Dr. Harvey Rosen's principles deserve to be considered with great interest: "Soft-tissue goals must influence the direction and extent of skeletal displacements" [START_REF] Rosen | Aesthetic Perspectives in Jaw Surgery[END_REF] . This component is mainly acquired through personal experience gained from contact with patients and an artistic sense. The latter is forged away from the operating theaters. It is developed while contemplating arts, discovering museums, observing nature and overall increasing one's general culture about mankind and its societies.

The third component is the patient's demand for a treatment. The orthodontist-surgeon team must carefully assess the actual reason(s) that make the patient seek an orthodontic-surgical protocol. It must be emphasized that even if most patients bring forward functional complaints during the first consultation (ie speech problems, masticatory disorders, TMJ dysfunctions, periodontal health issues etc), the core reason usually motivating patients for these procedures is an improvement of their facial aesthetics [START_REF] Patcas | Motivation for orthognathic treatment and anticipated satisfaction levels-a two-centre cross-national audit[END_REF] . In other words, being a well-trained artistically-educated physician is just not enough if you don't meet the actual patient's demand.

As far as the first component (Basic Knowledge) is concerned, there is little doubt that machines have by far exceed human capacities in storing and organizing data. Today, no human physician can compete with a computer as far as memorization of data is concerned. That's why the IBM-Watson system beat the best human players at Jeopardy -a general knowledge game -back in 2011. And, the amount of data for each patient has grown to a point where we are unable to comprehend it all in its entirety. However, while Silicium brains easily beat their biological counterparts in terms of pure theoretical knowledge, they lag far behind in the field of autonomous technical and procedural expertise. Indeed, robotics in surgery have primarily developed in the field of minimally-invasive surgery but it remains a very costly process [START_REF] Troccaz | Frontiers of Medical Robotics: From Concept to Systems to Clinical Translation[END_REF] . Thus its development has been hindered.

The second component is linked to the artistic sense of the surgeon. Here again we are faced with a balance of advantages and limitations of AI over the human brain that is interesting to consider.

Establishing a diagnosis requires a formal static and dynamic facial aesthetic examination. Deep learning techniques based on neural networks (and more specifically convolutional neural networks) proved very efficient at image recognition as proven by Patcas et al's demonstration of AI's ability to assess the impact of orthognathic treatment on facial attractiveness and estimated age. [START_REF] Patcas | Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age[END_REF] Thus, it can be hypothesized that in a near future deep learning algorithms educated with hundreds of thousands of expert-labelled clinical cases will be at least equivalent to the best maxillofacial surgeons at establishing the diagnosis of a dentofacial dysmorphism. Computer-aided facial recognition tools are already routinely used to help doctors diagnose rare craniofacial genetic disorders. [START_REF] Hurst | Facial recognition software in clinical dysmorphology[END_REF] However, AI has yet to demonstrate its value and capacities in the field of simulation of outcomes of orthognathic surgery. Indeed, the question of the virtual simulation of the postoperative aesthetic result remains unanswered to date. Skeletal displacements induce soft-tissue alterations that are very difficult to predict, especially in the nasolabial region, as the skin, subcutaneous adipose tissues, muscles and mucosa have distinct Young's modulus. The soft tissue response to the underlying skeletal displacements can be quite different according to the quality of the soft tissues linked to various factors such as (age, sex, ethnics, body max index, etc) and vary from one patient to the other. Therefore, it seems still very unlikely that an algorithm could accurately predict the planned final aesthetic result after an orthognathic surgery in a near future. Although this information is of key importance to most (if not all) patients, modern maxillofacial surgeons address this subject with their patients with great care on the basis of their own experience and clinical examination.

Not the least to consider is the third component in the elaboration of a treatment plan. Patient satisfaction is the ultimate goal the orthodontist-maxillofacial surgeon team should aim for. This satisfaction is linked to the appreciation by the patient that the treatment achieved the planned objectives. As stated above, accurately assessing the core motivation(s) of a patient seeking an orthodontic-surgical treatment requires a very fine analysis from the physician. Patients' demands are often ambiguous in the field of craniofacial dysmorphisms and it needs all the experience of a well-trained physician to detect patient's real motivation(s) and elaborate a personalized treatment plan. Beyond the natural empathy that should be part of the doctor-patient relationship, the orthodontist and maxillofacial surgeon should really try to "comprehend" (etymologically "to take with oneself") the patient's symptoms and demands. And, likewise the agreed upon treatment plan should be "comprehensible" by the patient so that he can really be involved in it. We believe this third component is definitely the one that demands the more human skills, as machines are not at ease when dealing with ambiguous data.

On the other hand, an overdependence to the enhanced capacities that AI offers in every step of the process of an orthodontic surgery protocol carries an obvious inherent risk: loss of intrinsic specialist skills . By allowing the practitioner to rely too heavily on computerized algorithms and simply execute established treatment plans, practitioners could lose their clinical and technical sense of reasoning in the long term. [START_REF] Makaremi | The role of cognitive sciences in orthodontic treatments: optimizing the interface between practitioners and new technologies[END_REF] The lack of use of specific skills learned and acquired by clinical experience could suffer if critical thinking is not used in conjunction with these new methods. [START_REF] Makaremi | Les enjeux d'une nouvelle interface. Guiral-Desnoës H[END_REF]

Conclusion

The number of digital tools available based on artificial intelligence will have an immense impact on orthognathic-surgical treatment plans from the initial diagnosis to follow-up treatment. This paradigm change brings with it several obvious beneficial effects:

-The passage from 2D to 3D imagery along with added benefits of increased diagnostic precision.

-The possibility to visualize the treatment plan and engage patients and practitioners in a constructive dialogue. This limits confusion and brings a unique treatment plan for each patient using the CAD/CAM technology.

-The assistance and collaboration of a software capable of assisting practitioners in all steps from treatment planning to treatment follow up. Such a software would have the ability to learn from every real-life case it is exposed to improve its performance on the following cases. Therefore, it is the responsibility of the entire medical profession to successfully manage the use of artificial intelligence in the diagnosis and treatment of patients and to achieve a positive symbiosis between clinical sense and AI. In order to achieve this, artificial intelligence needs to be viewed as a tool to be used with the utmost care and training, and not as a threat.

Figure legend

Fig 1 :

 1 Fig 1 : Digital workflow on surgical orthodontic protocols.

Fig 2 :

 2 Fig 2: CephX uses artificial intelligence and machine learning technologies to automate cephalometric analysis 19

Fig 3 :

 3 Fig 3: With CAD/CAM technology the biomechanics of alveolar decompensation could be incorporate on the appliance Insignia (Ormco).

Fig 4 :Fig 5 :Fig 6 : 3 D

 4563 Fig 4: Realization of digital casts (Ormco), virtual construction of the surgical splint (Dolphin imaging), 3D printing of surgical splints (AAO) `


Fourcade Arthur
email: arthur.fourcade@ch-gonesse.fr

Roman H Khonsari

Hossein

Deep learning in medical image analysis: a third eye for doctors

Keywords: Deep learning, artificial intelligence, neural network, image analysis, systematic review, computer vision

Artificial intelligence (AI) in medicine is a fast-growing field. The rise of deep learning algorithms, such as convolutional neural networks (CNNs), offers fascinating perspectives for the automation of medical image analysis. In this systematic review article, we screened the current literature and investigated the following question: 'Can deep learning algorithms for image recognition improve visual diagnosis in medicine?'

MATERIALS & METHODS

We provide a systematic review of the articles using CNNs for medical image analysis, published in the medical literature before May 2019. Articles were screened based on the following items: type of image analysis approach (detection or classification), algorithm architecture, dataset used, training phase, test, comparison method (with specialists or other), results (accuracy, sensibility and specificity) and conclusion.

RESULTS

We identified 352 articles in the PubMed database and excluded 327 items for which performance was not assessed (review articles) or for which tasks other than detection or classification, such as segmentation, were assessed. The 25 papers included were published from 2013 to 2019 and were related to a vast array of medical specialties. Authors were mostly from North America and Asia.

Large amounts of qualitative medical images were necessary to train the CNNs, often resulting from international collaboration. The most common CNNs such as as AlexNet and GoogleNet, designed for the analysis of natural images, proved their applicability to medical images.

CONCLUSION

CNNs are not replacement solutions for medical doctors, but will contribute to optimize routine tasks and thus have a potential positive impact on our practice. Specialties with a strong visual component such as radiology and pathology will be deeply transformed. Medical practitioners, including surgeons, have a key role to play in the development and implementation of such devices.

Introduction 1. Artificial intelligence and image analysis

Artificial intelligence (AI) in medicine is a fast-growing field, generating hopes and raising perplexing issues. AI can be defined as the ability for a computer to mimic the cognitive abilities of a human being. AI corresponds to a large array of techniques. Among them, machine learning is one of the most relevant approaches in the medical field. Three converging technical progresses have facilitated the medical applications of machine learning: [START_REF] Fourcade | Apprentissage profond : un troisième oeil pour les médecins[END_REF] the global rise of 'big data', that is the constitution and the analysis of very large databases, (2) the spectacular increase of the computing power of processors and (3) the design of new deep learning algorithms.

These technological advances allowed algorithms to process images and sounds, and be integrated into the digital tools of everyday life and into automated professional workflows.

The medical field faces a current massive growth in volume, complexity and heterogeneity of raw data. Effective and medically-focused big data analysis is a major issue in public health. AI offers three promising perspectives in this field: [START_REF] Fourcade | Apprentissage profond : un troisième oeil pour les médecins[END_REF] risks prediction via correlations analyses, [START_REF] Mccarthy | A proposal for the Dartmouth Summer Research Project on Artificial Intelligence[END_REF] genomic analysis and phenotype-genotype association studies, (3) automation of medical image analysis. In this report, we focused on the third topic and investigated the following question: 'Can image recognition deep learning algorithms improve medical visual diagnosis?' [START_REF] Fourcade | Apprentissage profond : un troisième oeil pour les médecins[END_REF].

Historical background

The fundamentals of AI were formalized in the 1950s [START_REF] Mccarthy | A proposal for the Dartmouth Summer Research Project on Artificial Intelligence[END_REF]. Initially, two contrasting conceptions of AI were developed: (1) cognitivism, that is the development of rule-based programs referred to as expert systems [START_REF] Miller | Internist-1, an experimental computer-based diagnostic consultant for general internal medicine[END_REF][START_REF] Shortliffe | Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system[END_REF] and [START_REF] Mccarthy | A proposal for the Dartmouth Summer Research Project on Artificial Intelligence[END_REF] connexionism, corresponding to the development of naive programs educated secondarily by data. Connexionism currently dominates the world of AI.

Algorithms known as artificial neurons, organized into artificial networks, have led to the design of programs that can be 'educated' [START_REF] Rosenblatt | The Perceptron: A probabilistic model for information storage and organization in the brain[END_REF]. The first neural networks were designed in the 1960s.

They consisted in sets of artificial neurons organized into superimposed layers. After going through an input layer for presentation purposes, data was analyzed via intermediate layers and ended into an output layer that produced a result. The learning abilities of neural networks were exploited only in the 1990s, with the initiation of 'deep learning', which corresponded to the development of very large multi-layered neural networks [START_REF] Lecun | Deep learning[END_REF]. In addition to the development of big data analysis and to the increase in computation power, deep learning was boosted in the years 2010 due to the development of a certain type of neural network known as Convolutional Neural Networks (CNN).

CNNs had specifically high performances in the field of pattern recognition. Most of the theoretical background of CNNs derives from the achievements of a French computer scientist in the 1980s, Yann LeCun, who created the LeNET network [START_REF] Lecun | Backpropagation applied to handwritten zip code recognition[END_REF]. LeNet was an automated handwriting recognition algorithm, intended to be used by banks for reading checks. In 2010, major international AI teams took part into the 'ImageNET Challenge', a competition during which they had the task to classify millions of natural images into thousands of different categories [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF]. The current navigation devices embedded into autonomous cars are derived from the networks designed by LeCun nearly forty years ago (Figure 1). CNNs are inspired by the structure of the primary visual cortex [START_REF] Hubel | Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[END_REF]; image recognition proceeds from the automatic extraction of the visual features of images. Automatic extraction is a breakthrough when compared with traditional manual extraction techniques, which were time consuming and less precise [START_REF] Shen | Deep learning in medical image analysis[END_REF]. CNNs are based on a specific multilayered architecture that can be trained by data (Figure 2).

Each individual piece of input data corresponds to an image associated with a label (for instance a medical diagnosis for a chest X-ray). The process of training a CNN with labelled images is known as supervised learning. During training, the network analyzes the image, considered as a matrix composed of pixels of varying intensities, by transiting it through the different layers. Some layers have the ability to modify the input data using features extraction, and are thus able to produce abstract images determined by the level of extracted features; others layers reduce the data into vectors, that are finally interpreted as probabilities by the last layers: 'Is the image on the chest X-ray a lung cancer?', 'Is this stain on the skin a melanoma?' (Figure 3).

Initially, the naive network, before proper training, labels the input pictures with a certain amount of error. Errors are then back-propagated [START_REF] Rumelhart | Learning representations by back-propagating errors[END_REF] within the network, which will be able to correct the characteristics of its neurons to match the proper label of the input image. The CNN thus extracts the features of the images uploaded during the training process and keeps the memory of these features into its neural layers. After an efficient training process involving a large input dataset, the network will be able to classify new un-labelled data and provide results as label categories.

Material and Methods

Here we provide a systematic review of the publications using CNN technology for medical image analysis, available in the National Library of Medicine database (PubMed). The search equation was the following: (convolutional OR deep learning) AND (classification OR detection) AND (image OR photography), filtered for 'Human studies' and 'Title/Abstract' as search fields.

The selected articles were screened according to a standard grid containing the following

Results

We identified 352 articles in the PubMed database based on our search equation between 01/01/2013 and 05/20/2019. We excluded 327 articles where CNNs performance was not assessed (review articles) or where tasks other than detection or classification, such as segmentation, were assessed (Figure 5). The 25 papers included were ordered according to medical specialties: dermatology [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF], ophthalmology [START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][18], cardiothoracic imaging [START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF], senology [START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF],

oral and maxillofacial surgery [START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][32], hepato-gastro-enterology [START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF]. Demographic datas about the included papers and informations on the journals in which the works were published are provided in Tables 1 and2. These results are summarized in Figures 6 and7, which showed a stable increase of the volume of publications on CNN performances in the last few years: 1 paper in 2013 [START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF], 8 papers in 2016 [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF], 6 papers in 2017 [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF], 5 papers in 2018 [START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF] and already 5 papers in 2019 [18,[START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF]32,[START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF]. Our results furthermore indicate that most of the papers were produced by North American and Asian teams: 12 papers were from the USA and Canada [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF] and 13 papers were written by Asian teams [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][18][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][32][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF]. Interestingly, international collaboration between Asian / South American teams and European / North American teams allowed compiling very large databases in 4 cases [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF]. The assessment of the characteristics of the networks (table 3) showed that most teams used previously developed codes such as GoogleNet [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF]32,[START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF] and AlexNet [START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF]. Most of the 25 networks were pre-trained [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][18][START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][32][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF], and most teams had included proofs of veracity for the labeling of the images, based on standard methods depending on the dataset used in the study, such as for instance histological confirmation [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF] or assessment by 1-4 specialists (table 4) [START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][18][START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF]32,[START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF]. The quantity of included images (table 5) was variable and was not very informative given the variety of the datasets: from 170 pictures of skins lesions used for melanoma detection [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF] to 139886 fundoscopy images used for diabetic retinopathy diagnosis [START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF]. Only 5/25 papers compared the performances of the networks to medical professionals [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF]32,[START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF]; nevertheless, 13/25 studies compared the performances of the networks to traditional detection or classification techniques [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF][32][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF]. The results of the quantitative assessment of performances of the networks in all studies were satisfactory; 18 figures were provided by the authors: the lowest precision score was 0.75 [START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF] and the lowest sensibility was 0.70 [START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF]. All other values of precision, sensibility, specificity and AUC (area under curve) were between 0.8 and 0.9 [START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF] or over 0.9 [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][18][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][32][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF]. Only 9/25 studies provided visualization methods in order to better understand the inner mechanisms of the network (table 6) [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF]18,[START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF].

Discussion

Most of the studies included into this review have been published within the last three years, and authors are from North America and Asia. These demographics underline the need for European Countries to launch specific plans in order to promote medical IA. Several French initiatives are The choice of the network architecture is conditioned by specific tasks. Nevertheless, most authors have used previously developed networks already efficient on natural images, such as AlexNET [START_REF] Krizhevsky | ImageNet classification with deep convolutional neural networks[END_REF] and GoogleNET [START_REF] Szegedy | Rethinking the inception architecture for computer vision[END_REF], especially in their pre-trained versions [START_REF] Shin | Deep convolutional neural networks for Computer-Aided Detection: CNN architectures, dataset characteristics and transfer learning[END_REF]. These networks easily run on softwares as Caffe [START_REF] Jia | Caffe: convolutional architecture for fast feature embedding[END_REF], Theano [START_REF] Bastien | Theano: new features and speed improvements. Deep Learn Unsupervised Featur Learn[END_REF] or Tensorflow [START_REF] Abadi | Tensorflow: largescale machine learning on heterogeneous distributed systems[END_REF]. All of these softwares are freely available online and are based on the widespread programing language Python. The data used in the 25 studies included macroscopic and microscopic images of different types: clinical photographs [START_REF] Esteva | Dermatologistlevel classification of skin cancer with deep neural networks[END_REF][START_REF] Nasr-Esfahani | Melanoma detection by analysis of clinical images using convolutional neural network[END_REF][START_REF] Gargeya | Automated identification of diabetic retinopathy using deep learning[END_REF][START_REF] Grinsven | Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF][START_REF] Ahn | A deep learning model for the detection of both advanced and early glaucoma using fundus photography[END_REF][18][START_REF] Uthoff | Point-of-care, smartphonebased, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities[END_REF][START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF][32][START_REF] Jia | A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images[END_REF][START_REF] Urban | Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy[END_REF][START_REF] Horie | Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[END_REF][START_REF] Alaskar | Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images[END_REF], pathology slides [START_REF] Ciresan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF], X-rays [START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF][START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF][START_REF] Zech | Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study[END_REF][START_REF] Taylor | Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study[END_REF][START_REF] Arevalo | Representation learning for mammography mass lesion classification with convolutional neural networks[END_REF][START_REF] Jadoon | Three-class mammogram classification based on descriptive CNN features[END_REF][START_REF] Arik | Fully automated quantitative cephalometry using convolutional neural networks[END_REF] and CT images [START_REF] Anthimopoulos | Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[END_REF][START_REF] Li | Pulmonary nodule classification with deep convolutional neural networks on computed tomography images[END_REF][START_REF] Miki | Classification of teeth in cone-beam CT using deep convolutional neural network[END_REF]. Four criteria are often cited in the literature as crucial elements for the design of reliable networks and are referred to as the four 'V'. (2) Veracity is conditioned by two quality criteria.

(a) Image quality -resolution, limited variability due to angle of view, zoom or brightness -often increased by pre-treatment [START_REF] Kohli | Implementing machine learning in radiology practice and research[END_REF].

(b) Label quality, depending on the expertise of the practitioners who created the database. Database review by a scientific committee is thus recommended before training.

(3) Velocity, related to power of the processor. GPU, made of several parallel cores, are well fitted for CNNs, specifically during the training phase when a lot of images are simultaneously processed.

CNN training is a key step, for which specific technical skills are required, in order to avoid overfitting [START_REF] Caruana | Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping[END_REF] on limited data, leading to issues for when using the network to analyze wider datasets.

Training thus requires evaluation and monitoring.

Transparency is a fundamental parameter in medical AI. CNNs are opaque structures often referred to as 'black boxes'. Some systems offer partial visualization techniques (heat maps, probability maps) in order to provide some view of the inner functioning of the CNN. The understanding of how these networks 'work' is a relevant and major challenge in medical AI.

Nevertheless, using computer vision devices that have 'non-human' image analysis abilities is by itself potentially fruitful as hidden correlations between images can potentially be uncovered based on parameters that are not perceived by the human brain, even by the brain of a trained expert. AI has clearly shown its efficiency in several clinical visual tasks, but comparative clinical studies showing the integration of this technique into clinical workflows are still missing for most applications. Nevertheless, both the robustness of the current results and the potentially simple interfaces that can be designed using trained CNNs provide bases for straightforward, time-saving, reliable and practical applications. CNNs could then be conceived as colleagues providing expert second opinions on tricky clinical questions. In addition, CNNs are intrinsically not subjected to confusing factors such as tiredness, individual beliefs or hierarchical issues, and thus minimize inter-and intra-individual variabilities when achieving a specific task.

However, adverse effects of the use of CNNs consist in deskilling, phenomenon described during the automation of any type of task. Another non-intended consequence of the clinical use of CNNs could be an overreliance to the machine by the practitioner. This could lead to the omission of crucial factors, such as clinical data, that the machine does not integrate into its decision-making process. Indeed, CNNs answer specific questions and sole visual criteria remain insufficient to perform a diagnosis in most cases. Some authors have suggested combining CNNs with algorithms screening other categories of data (for instance clinical, biological, molecular). In brief, networks provide a certain decision with a given probability of accurateness, which is then to integrate into a wider array of diagnostic arguments.

A key point of medical application of CNNs is that the process directly involves the patients themselves. Melanoma screening methods can be used as self-medicine devices, and more generally, CNNs can be seen as solutions improving management and prognosis by promoting earlier diagnosis and earlier treatment. This point raises the confidence patients will place into these devices: excessive or minimal confidence in CNNs may interfere within the relationship between patients and doctors.

The medical use of CNNs will only be successful via collaboration between several actors, each having a specific and fundamental role in the process.

(1) Patients. They are the source of the data and their consent is required for its use.

(2) Practitioners. They define relevant medical questions for which CNNs could be of use and collect the data, which they interpret and label. They are then crucial for the validation of the CNNs in a clinical environment. With the help of the digitization of the medical data, all the actors involved in the design and practical application of CNNs will interact in a fruitful manner, thanks to the optimization of data collection, interpretation, storage, sharing and use. Multi-centric platforms will contribute to augment data volume and veracity.

Conclusion

CNNs are not replacement solutions for medical doctors, but will contribute to optimize routine tasks and thus have a potential positive impact on our practice. Specialties with a strong visual component such as radiology and pathology will be deeply transformed by CNNs but all the fields of medical and surgical practice will be affected by this technology.

The role of practitioners is key for the development and implementation of such devices.

Medical doctors currently have a historical chance to take part into a scientific revolution by understanding deep learning, taking part in the conception and evaluation of new devices but also by contribution to conceive a framework for the regulation of this new type of medical activity.

items: ( 1 )

 1 aim of the study: detection or classification, (2) methods: network architecture, dataset, training, validation, test, comparison method (with specialists or other), (3) results: accuracy, sensibility and specificity and (4) conclusion. More precisely, within the methods section, the following data regarding network architecture were collected: (a) pre-training (yes/no), (b) network parameters, (c) layers (convolution and pooling, fully connected and softmax) and (d) software. Datasets were characterized using the following parameters: (a) variety / veracity: origin and certification, (b) volume: number, size, pre-training, augmentation, set distribution, (c) speed (Graphic Processor Unit (GPU) type) (Figure 4).

  promising and may contribute to build an EU medical AI community, such as the PaRis Artificial Intelligence Research InstitutE (PRAIRIE), and the development of specific training programs at the University of Paris (the largest medical school in Europe), with a dedicated international Master's Degree, or at CentraleSupelec in association with Inria Saclay (two high-level engineering schools and research centers).

( 1 )

 1 Volume and Variety. The training phase requires a significant number of images. Beyond the quantity, heterogeneous databases increase robustness. Augmentation techniques (rotatingcutting -resizing the images) are often used to increase Volume and Variety without decreasing the third V, Veracity.

( 3 )

 3 Engineers. Specific training programs for medical AI are required, based on collaborations between academic hospitals, medical schools and engineering schools.

( 4 )

 4 Entrepreneurs and investors. By partnering with medical doctors, investors create the financial conditions required to hire competent engineers and benefit from powerful datacenters and computation power.

( 5 )

 5 Lawmakers. Data protection institutions, such as the Commission Nationale de l'Informatique et des Libertés (CNIL) and the Institut National des Données de Santé (INDS) in France supervise the use of clinical data and the application of these medical devices.Collaborations with the national medical councils (Conseil National de l'Ordre des Médecins in France) will result in the formulation of ethical and legal questions, mostly regarding medical.

Figure 1 .

 1 Figure 1. Concepts and theories in deep learning.

Figure 2 .

 2 Figure 2. Structure of a typical artificial neural network: the multilayered Perceptron.

Figure 3 .

 3 Figure 3. Structure of a Convolutional Neural Network (CNN).

Figure 4 .

 4 Figure 4. Canvas for data extraction from the included articles.

Figure 5 .

 5 Figure 5. Inclusion process for the systematic review.

Figure 6 .

 6 Figure 6. Geographical origin of the included publications.

Figure 7 .

 7 Figure 7. Number of scientific publications per year related to medical imaging interpretation and deep learning.

Table legends Table 1 .

 legends1 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Year of publication -

	Journal -Impact Factor.

Table 2 .

 2 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Origin -Team -Objective.

Table 3 .

 3 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Network -Training step.

Table 4 .

 4 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Dataset quality

Table 5 .

 5 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Dataset size.

Table 6 .

 6 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Results and comparisons.

	Radiology	7.296

Table 1 .

 1 Systematic review of deep learning applications in medical image analysis. Articles assessing detectionand classification abilities of neural networks. Origin -Team -Objective.

	Authors	Origin	Team	Objective
	DERMATOLOGY
	Esteva & al (12)	California, USA	Mixed 1	Skin lesion classification
	Nasr-esfahani & al (13)	Isfahan, Iran / Michigan, USA	Mixed	Melanoma detection
	OPHTALMOLOGY
	Gargeya & al (14)	California, USA	Mixed	Diabetic retinopathy detection
	Grinsven & al (15)	Nijmegen, Netherlands	Engineers Retinal hemorrhages detection
	Gulshan & al (16)	California, USA / Texas, USA / Madurai, India	Mixed	Diabetic retinopathy detection
	Ahn & al (17)	Seoul, Korea	Mixed	Glaucoma detection
	Phan & al (18)	Tokyo, Japan	Mixed	Glaucoma detection
	THORACIC
	Anthimopoulos & al (19)	Bern, Switzerland	Engineers	Interstitial lung disease classification
	Cicero & al (20)	Toronto, Canada	Mixed	Thoracic disease classification
	Lakhani & al (21)	Pennsylvania, USA	Mixed	Tuberculosis detection
	Li & al (22)	Shenyang, China	Mixed	Pulmonary node detection
	Zech & al (23)	California, USA	Mixed	Pneumonia detection
	Taylor & al (24)	California USA	Mixed	Pneumothorax detection
	SENOLOGY
	Arevalo & al (25)	Bogota, Colombia / Aveiro, Portugal	Mixed	Lesions classification on mammograms
	Jadoon & al (26)	London, UK / Islamabad, Pakistan	Engineers	Lesions classification on mammograms
				Mitosis detection on
	Ciresan & al (27)	Lugano, Switzerland	Engineers	histological slices of breast
				tumors
	O-M-F SURGERY
	Arik & al (28)	California, USA	Mixed	Anatomical point-of-interest detection and classification

1

Team of engineers and medical doctors.

Table 1 .

 1 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Network -Training step.

Table 1 .

 1 Systematic review of deep learning applications in medical image analysis. Articles assessing detectionand classification abilities of neural networks. Dataset size.

	Authors	Initial dataset	Augmentation techniques	Training & validation dataset	Test dataset
	DERMATOLOGY
	Esteva & al (12)	129405	Yes	127463	1942
	Nasr-esfahani & al (13)	170	Yes (x36)	4896	1224
	OPHTALMOLOGY
	Gargeya & al (14)	77348	Yes	75137	1748 + 463
	Grinsven & al (15)	7879	Yes	5287	2592
	Gulshan & al (16)	139886	No	128175	9963 + 1748
	Ahn & al (17)	1542	Yes	1078	464
	Phan & al (18)	3312	Yes	75%	25%
	THORACIC
	Anthimopoulos & al (19)	14696 images from 120 CT scans	Yes	13646	1050
	Cicero & al (20)	35038	Yes	32586	2443
	Lakhani & al (21)	1007	Both	857	150
	Li & al (22)	62492 images from 1010 CT scans	No	54680	7811
	Zech & al (23)	158323	-	80%	20 %
	Taylor & al (24)	13292 (3107 pneumothorax)	Yes	85%	15%
	SENOLOGY
	Arevalo & al (25)	736	Yes (x8)	442	294
	Jadoon & al (26)	2796	Yes (x7)	-	-
	Ciresan & al (27)	300 images of mitosis from 50 histological slices	Yes	35 (66000 mitosis & 151 million non-mitosis)	15
	O-M-F SURGERY

Table 1 .

 1 Systematic review of deep learning applications in medical image analysis. Articles assessing detection and classification abilities of neural networks. Results and comparisons. AUC: area under curve; Sp: specificity;

Sn: sensitivity.

Cicero & al[START_REF] Cicero | Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[END_REF] 2017 Investigative Radiology 5.195Lakhani & al[START_REF] Lakhani | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks[END_REF] 2017

Yes

Phan & al

Authors


A Rabuel

N Srouji

D Zwetyenga
email: nzwetyenga@gmail.com

Guilier

Vivien Moris
email: morisvivien@gmail.com

Sarra Cristofari
email: sarra.cristofari@aphp.fr

Alessio Stivala
email: alessiostivala@gmail.com

Barbara Lehre

Charline Gengler
email: charline.gengler@chu-dijon.fr

Valentin Rabuel
email: valentin.rabuel@chu-dijon.fr

Alexandre Srouji
email: alexandre.srouji@chu-dijon.fr

Narcisse Zwetyenga

David Guilier

David Guillier
email: docteurguillierdavid@gmail.com

Fluorescent indocyanine green angiography: Preliminary results in microsurgery monitoring

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Pedicled flaps and free-tissue transfer flaps are used routinely to reconstruct head and neck, limb, hand, thoracic and abdominopelvic hard and soft tissue defects. But failure remains a constant concern.

Usually failure is due to blood supply compromise. To detect vascular complication clinical monitoring, based on subjective criteria, remains the gold-standard. Clinical monitoring can be improved by many intraoperative and postoperative monitoring devices. These noninvasive and invasive devices help prevent and identify vascular occlusion, with varying degrees of success. None of these devices are universally adopted [START_REF] Yeoh | Fluorescence angiography in the assessment of flap perfusion and vitality[END_REF]. Noninvasive techniques include hand-held Doppler ultrasound, infrared thermography, polarized spectral imaging, and laser Doppler perfusion imaging [START_REF] Cornelissen | Near-infrared fluorescence image-guidance in plastic surgery: A systematic review[END_REF].

Indocyanine green (ICG), a fluorescent dye, has been used for more than 40 years for cardiac output, liver function, neurosurgery, ophthalmology and digestive surgery [START_REF] Levesque | Non-invasive ICGclearance: a useful tool for the management of hepatic artery thrombosis following liver transplantation[END_REF][START_REF] Killory | Prospective evaluation of surgical microscope-integrated intraoperative near-infrared indocyanine green angiography during cerebral arteriovenous malformation surgery[END_REF][START_REF] Diana | Enhanced-reality video fluorescence: a real-time assessment of intestinal viability[END_REF]. ICG is a water-soluble dye that absorbs light in the near-infrared spectral range, with a peak at 805 nm, and emits fluorescence at 835 nm. ICG completely binds to plasma proteins after intravenous injection, and is exclusively distributed in the intravascular space [START_REF] Muckle | Plasma proteins binding of indocyanine green[END_REF]. ICG has a short plasma half-life of 3.4 minutes. That allows repeated injections without reaching toxic levels. IGC is cleared from blood by the liver and excreted into the bile [START_REF] Hitier | Indocyanine green fluorescence angiography for free flap monitoring: A pilot study[END_REF]. Adverse effects are rare (anaphylactic shock, hypotension, dyspnea, nausea, exanthema, and pruritus) [START_REF] Obana | Survey of complications of indocyanine green angiography in Japan[END_REF]. These properties make it a suitable tracer for vessel perfusion.

The fluorescent indocyanine green angiography (FA ICG) has been used in thyroid surgery, in sentinel node procedure (breast cancer).

The objective of this study is to evaluate preliminary the results of the fluorescent indocyanine green angiography in free flaps procedures.

Materiel and methods

Patients who had microsurgical flap reconstruction were included during the study period in a single center.

The FA ICG was used at specific times. Intra-veinous injections of 0.1 mg/kg of INFRACYANINE® (concentration 2.5 mg/mL) were done intraoperatively. The Fluobeam® device (figure 1), programmed on sensitivity and mapping to interpret the data, was used.

Peroperative

The first injection was done during the beginning of the flap harvesting. Skin paddle perforators vessels were checked (size, path) to identify and spare the vessels and to adapt the skin paddle's shape to avoid necrosis (figure 2).

If it is necessary to perform osteotomies, another injection has been performed to check the osseous perforators vessels and to adapt the sites of osteotomies (fibula or radius) (figure 3).

Another injection was performed at the end of vascular anastomosis to visualize the arterial and venous patency (figure 4).

A final peroperative injection, was performed after the modeling, positioning and suture of the flap. Cutaneous, muscular and osseous perfusion were checked. Attention was payed to skin paddle edges because of malposition and/or hard suture (figure 5). Time of FA ICG procedure was measured.

Postoperative

Clinical features of the flap (color, time of recoloration, temperature, etc.) were assessed every two hours.

Every eight hours, for practical reasons, FA ICG was used to monitor the flap during 3 days.

Results

Twelve patients enrolled were 9 males and 3 females. Their mean age was 54.5 years (range 25 -75 years). Details of the results are shown in the Table 1.

Eight patients had free flaps: 4 fibular flaps (3 for mandibular reconstruction and 1 femur reconstruction), 2 radial forearm flaps (maxillary reconstruction), 1 latissimus free flap (tibia soft-tissue coverage) and 1 retroauricular fasciocutaneous free flap (thumb soft-tissue coverage).

Four flaps were pedicled flaps: 2 saphenous flaps for tibia soft-tissue coverage and 2 local flaps (Antia-Buch and frontal flaps) were realized for nose and ear defects respectively.

Eight patients had peroperative modifications to avoid potential complications: design modification of the skin paddle was made in 3 cases, tension stitches release was made in 2 cases, site of the osteotomy modification was made in 2 patients, surgical reexplorations was made in 2 patients and pivot point modification was made in 1 patient. Therefore, several modifications could have been made to one patient. In one case, the FA ICG showed hypoperfusion of the skin paddle allowing a precocious salvage reintervention. Peroperative exploration showed arterial and venous thrombosis due to a compressive hematoma in the pedicle area. For another patient, failure occurred despite vascular revision (Table 1). Every eight hours, FA ICG was used to monitor the flap. The follow-up did not show particular event.

The average time of FA ICG added to the surgical time was 10 minutes.

Discussion

FA ICG could be a reliable method for monitoring free-tissue transfers.

Evaluation of microvascular flap perfusion is still based on subjective clinical features [START_REF] Jones | Clinical monitoring of free flaps in the UK[END_REF]. Clinical monitoring is observer-dependent and does not allow information sharing, test reproducibility, and consistent postoperative follow-up.

Free flap failure remains a difficul event for both patient and surgeon. The successful of salvage rate is linked to the delay between the onset of ischemia and its clinical assessment.

Various instrumental methods have been described, such as implantable doppler probe [START_REF] Clert | Implantable doppler probe for microsurgical free flap monitoring in cervico-facial reconstructive surgery[END_REF], color duplex ultrasound [START_REF] Few | Monitoring buried head and neck free flaps with high-resolution color-duplex ultrasound[END_REF], near-infrared spectroscopy (NIRS) [START_REF] Repez | Continuous postoperative monitoring of cutaneous free flaps using near infrared spectroscopy[END_REF] and laser Doppler flowmetry [START_REF] Yuen | Monitoring free flaps using the laser Doppler flowmeter: five-year experience[END_REF]. Fluorescence angiography with indocyanine green (FA ICG) is a relatively new method for evaluating tissue perfusion for flaps.

FA ICG was performed every 8 hours for practical reasons (lack of specialized personnel). With experience, FA ICG monitoring should be done at the same time as the clinical examination.

FA ICG has several advantages: -Noninvasive procedure.

-During operation, the possibility of identifying the correct position of the perforator vessels to allow the design of the skin paddle to maximize the perfusion and to plan osteotomies sites. -Facilitate harversting and modelling the flap for the young surgeon.

-Clinical patency test has a low sensitivity in the diagnosis of intravascular luminal obstruction [START_REF] Few | Monitoring buried head and neck free flaps with high-resolution color-duplex ultrasound[END_REF], especially when the vascular flow is low. FA ICG is able to make difference between low flow and vascular thrombosis. Precocious diagnosis and salvage therapeutic of the flap can be realized [START_REF] Yeoh | Fluorescence angiography in the assessment of flap perfusion and vitality[END_REF][START_REF] Cornelissen | Near-infrared fluorescence image-guidance in plastic surgery: A systematic review[END_REF].

Conclusion

FA ICG procedure can help in monitoring of free-flaps to improve success rate. In, perspective FA ICG may be used in monitoring of local and regional flaps.

This technique may be used such a pedagogical tool for young practitioners in their first microsurgery procedures.

Figure 1 :

 1 Figure 1: Fluobeam device

Figure 2 :Figure 3 :Figure 4 :Figure 5 :

 2345 Figure 2: Skin paddle perforator vessel during a fibula flap harvesting