Multimedia data mining for automatic diabetic retinopathy screening

International audience— This paper presents TeleOphta, an automatic sys-tem for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%

Exudate detection in color retinal images for mass screening of diabetic retinopathy

International audienceThe automatic detection of exudates in colour eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to auto-matically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also de-tect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods

Télédermatologie : évaluation d’un besoin par les réseaux sociaux

International audienceObjectif : Si le bénéfice de la télédermatologie (TD) est partagé par les professionnels, dans un contexte de réduction budgétaire la pérennisation de ses modèles de financement est à l'étude en l'absence de tarification spécifique. Partant de l'hypothèse que les utilisateurs des réseaux sociaux sont potentiellement les utilisateurs/patients de la TD demain, notre objectif était d'évaluer leur connaissance et leur intérêt pour cette nouvelle activité médicale. Matériel et méthode : Diffusion d'un web questionnaire incluant pour chaque sondé : leurs caractéristiques, leur attitude face à un problème dermatologique, leur connaissance de la TD. La TD était définie par la demande d'avis spécialisé via un professionnel de santé alors que la e-dermatologie (ED) permettait une sollicitation directe pour un conseil sans prescription (images + renseignements). Le questionnaire était diffusé de manière virale en sollicitant initialement les membres d'un laboratoire de génie industriel (n = 8) sans lien avec le milieu médical. Seules les réponses des ≥ 18 ans étaient incluses. Les facteurs associés à un intérêt pour la TD ou la e-dermatologie étaient analyses en univarié, puis par régression logistique si p < 0,15. Résultats : Trois cent dix-neuf réponses ont été collectées en 1 semaine (âge moyen 42 ans ± 15, sex-ratio F/M 2,3). La première catégorie socioprofessionnelle représentée était cadre ou profession intellectuelle 57 %. Les sondés avaient un revenu moyen entre 2000–3000 € dans 60 % des cas. Trente-sept pour cent avaient des dépenses annuelles pour leur peau de 100–500 €, 43 % avait un dermatologue, 30 % n'avaient pas consulté depuis 5 ans. En cas de problème dermatologique, 8 % n'avaient rien fait, 44 % avaient vu un dermatologue, 52 % attendaient ≥ 15 jours pour consulter. Soixante et onze pour cent des sondés ne connaissaient pas la TD, 89 % se déclaraient intéressés en cas de besoin et avec un coût ≥ 25 € sans remboursement. Concernant ED, 81 % des sondés étaient intéressés pour savoir s'ils devaient consulter dans 59 % des cas, pour une information complémentaire 37 %, et de payer ≥ 25 € dans 54 % des cas. En analyse univariée les individus intéressés par la TD dépensaient significativement plus pour leur peau p = 0,028, et significativement plus intéressés à la ED ou prêts à payer pour TD et ED (p < 0,001). Pour la ED, on retrouvait significativement les mêmes associations, avec plus de cadres (p = 0,044). En multivarié, une association significative persistait entre intérêt pour ED et payer pour les 2 chez les individus intéressés par TD (p = 0,045, p = 0,030, p = 0,09). Discussion : Plus de 2/3 des cadres est inscrit sur les réseaux, ≥ 90 % ont une utilisation régulière d'Internet. Malgré une population d'étude biaisée par la méthode, il met en évidence dans une population cible et connectée un fort intérêt pour la TD/ED avec une acceptabilité à dépenser sans remboursement pour ce nouveau service notamment pour préparer une éventuelle visite chez le dermatologue

Studying Disagreements among Retinal Experts through Image Analysis

International audienceIn recent years, many image analysis algorithms have been presented to assist Diabetic Retinopathy (DR) screening. The goal was usually to detect healthy examination records automatically, in order to reduce the number of records that should be analyzed by retinal experts. In this paper, a novel application is presented: these algorithms are used to 1) discover image characteristics that sometimes cause an expert to disagree with his/her peers and 2) warn the expert whenever these characteristics are detected in an examination record. In a DR screening program, each examination record is only analyzed by one expert, therefore analyzing disagreements among experts is challenging. A statistical framework, based on Parzen- windowing and the Patrick-Fischer distance, is presented to solve this problem. Disagreements among eleven experts from the Ophdiat screening program were analyzed, using an archive of 25,702 examination records

Clinical Presentation of Covid-19 in Health Care Workers from a French University Hospital

International audienceNo abstract availabl

Spatial normalization of eye fundus images

International audienceThe development of digital retinal color imaging causes a substantial increase in the number and the size of retinal image databases. Image processing methods have been developed to help the specialists analyze these images. However, the heterogeneity of the databases, regarding image scale, contrast or quality, makes the design of generic image processing algorithms difficult. The presented work focuses on the spatial normalization of these images. The method is based on the definition of a size invariant in images. Unfortunately, the size of anatomical structures is either difficult to measure (e.g. the distance between optic disk and fovea requires a tricky segmentation of the fovea) or can change from one person to another (e.g. the optic disk size ranges from 1 to 2 mm). Neither does the resolution of images give a satisfactory solution, even if it is the approach used in the literature. We propose to use the diameter of the field of view in images as a size invariant. It is shown to give good results, provided that all the images have been acquired with the same aperture angle. OPHDIAT is a telemedicine network for diabetic retinopathy screening. Thousands of color eye fundus images have been collected, 70% of which have been classified as healthy by ophthalmologists. The TeleOphta project aims at performing a preliminary analysis of the images, in order to automatically filter out healthy images, and thus reduce the burden on specialists. The proposed method has been validated using images from OPHDIAT. Its results have been compared with those of the spatial normalization based on the manual measurement of the distance between the center of the optic disk and the center of the fovea. Results show a nearly perfect agreement between them

Spatial normalization of eye fundus images

International audienceThe development of digital retinal color imaging causes a substantial increase in the number and the size of retinal image databases. Image processing methods have been developed to help the specialists analyze these images. However, the heterogeneity of the databases, regarding image scale, contrast or quality, makes the design of generic image processing algorithms difficult. The presented work focuses on the spatial normalization of these images. The method is based on the definition of a size invariant in images. Unfortunately, the size of anatomical structures is either difficult to measure (e.g. the distance between optic disk and fovea requires a tricky segmentation of the fovea) or can change from one person to another (e.g. the optic disk size ranges from 1 to 2 mm). Neither does the resolution of images give a satisfactory solution, even if it is the approach used in the literature. We propose to use the diameter of the field of view in images as a size invariant. It is shown to give good results, provided that all the images have been acquired with the same aperture angle. OPHDIAT is a telemedicine network for diabetic retinopathy screening. Thousands of color eye fundus images have been collected, 70% of which have been classified as healthy by ophthalmologists. The TeleOphta project aims at performing a preliminary analysis of the images, in order to automatically filter out healthy images, and thus reduce the burden on specialists. The proposed method has been validated using images from OPHDIAT. Its results have been compared with those of the spatial normalization based on the manual measurement of the distance between the center of the optic disk and the center of the fovea. Results show a nearly perfect agreement between them

TeleOphta: Machine learning and image processing methods for teleophthalmology

International audienc