Parafoveal OCT Angiography Features in Diabetic Patients without Clinical Diabetic Retinopathy: A Qualitative and Quantitative Analysis

Purpose. To evaluate the capacity of OCT angiography (OCTA) for detecting infraclinical lesions in parafoveal capillaries in diabetic patients without diabetic retinopathy (DR). Methods. This prospective observational cross-sectional case-control study analyzed the superficial and deep capillary plexuses (SCP and DCP) on macular OCTA scans (3 × 3 mm) centered on the fovea. We compared 22 diabetic patients (34 eyes included) without DR diagnosis on color fundus photographs, with 22 age- and gender-matched nondiabetic controls (40 eyes included). Qualitative analysis concerned morphological ischemic capillary alterations. Quantitative analysis measured foveal avascular zone (FAZ) size, parafoveal capillary density, and enlargement coefficient of FAZ between SCP and DCP. Results. Neither the qualitative nor quantitative parameters were significantly different between both groups. No microaneurysms or venous tortuosity was observed in any of the analyzed images. On the SCP, the mean FAZ area was 0.322 ± 0.125 mm2 in diabetic patients and 0.285 ± 0.150 mm2 in controls, P=0.31. On the DCP, the mean FAZ area was 0.444 ± 0.153 mm2 in cases and 0.398 ± 0.138 mm2 in controls, P=0.20. Conclusion. OCTA did not detect infraclinical qualitative or quantitative differences in parafoveal capillaries of diabetic patients without DR in comparison with nondiabetic controls

CURRENT USE OF NANOPARTICLES IN ENDODONTICS: A SYTEMATIC REVIEW

Nowadays, technology evolves very fast and we are witnesses of major changes in dentistry. Nanotechnology improved many fields of dentistry, including endodontics. In this paper, we compared different irrigants and sealers which are currently used in endodontic treatments, their advantages and disadvantages and their limitations. In the context of emerging trends of nanotechnology in all fields of dentistry, we aimed to highlight the importance of developing new irrigants and sealers improved with nanoparticles, with superior properties compared to traditional ones. As resulted from our research, the most used irrigants in endodontics (NaOCl, EDTA, CHX) possess different kinds of advantages, but none is flawless, also having some limitations. Also, every current sealer available on the market has one or more disadvantages. For this reason, nanotechnology is very welcomed in this field and different kinds of nanoparticles were proposed for their particularities in order to improve the performances of endodontic materials. We present in this work a review of the literature regarding different types of nanoparticles, their effects on endodontic microbiota and also, their particularities

prevalence and phenotypes of age related macular degeneration in eyes with high myopia

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Optical Coherence Tomography Angiography to Distinguish Changes of Choroidal Neovascularization after Anti-VEGF Therapy: Monthly Loading Dose versus Pro Re Nata Regimen

Purpose. To compare the qualitative and quantitative choroidal neovascularization (CNV) changes after antivascular endothelial growth factor (anti-VEGF) therapy in treatment-naïve and treated eyes with age-related macular degeneration (AMD) using optical coherence tomography angiography (OCTA). Methods. Consecutive patients with neovascular AMD underwent multimodal imaging, including OCTA (AngioPlex, CIRRUS HD-OCT model 5000; Carl Zeiss Meditec, Inc., Dublin, OH) at baseline and at three monthly follow-up visits. Treatment-naive AMD patients undergoing anti-VEGF loading phase were included in group A, while treated patients were included in group B. Qualitative and quantitative OCTA analyses were performed on outer retina to choriocapillaris (ORCC) slab. CNV size was measured using a free image analysis software (ImageJ, open-source imaging processing software, 2.0.0). Results. Twenty-five eyes of 25 patients were enrolled in our study (mean age 78.32 ± 6.8 years): 13 treatment-naïve eyes in group A and 12 treated eyes in group B. While qualitative analysis revealed no significant differences from baseline to follow-up in the two groups, quantitative analysis showed in group A a significant decrease in lesion area (P=0.023); in group B, no significant change in the lesion area was observed during anti-VEGF therapy (P=0.93). Conclusion. Treatment-naïve and treated eyes with CNV secondary to neovascular AMD respond differently to anti-VEGF therapy. This should be taken into account when using OCTA for CNV follow-up or planning therapeutic strategies

Classification de maladies héréditaires rétiniennes par apprentissage profond sur des images d’autofluorescence du fond d’œil

Les maladies rétiniennes héréditaires (IRD) sont un groupe de maladies génétiques affectant la rétine, avec une prévalence estimée à 1 personne sur 3000, avec un total de plus de 2 millions de personnes dans le monde. En tant que cartographie métabolique in vivo de la rétine, l'imagerie par autofluorescence du fond d'œil (FAF) joue un rôle clé dans l'évaluation des patients atteints d'IRD, avec plusieurs phénotypes FAF décrits pour chaque IRD.Ce travail a comme objectif d’appliquer l'apprentissage profond (DL) au FAF dans les IRD.Premièrement, des images FAF de patients atteints de rétinite pigmentaire (RP), de la maladie de Best (BD), de la maladie de Stargardt (STGD1), ainsi que d'un groupe comparable des yeux normaux ont été utilisées pour entraîner un réseau neuronal convolutionel multicouche (CNN), afin de différencier sur les images FAF entre chaque type d'IRD et une FAF normale. Le CNN a été entraîné et validé avec 389 images FAF. Des techniques d'augmentation établies ont été utilisées. Un optimiseur Adam a été utilisé. Ensuite, le classificateur construit a ensuite été testé avec 94 images FAF n’ayant pas été utilisées pour l’entraînement. La visualisation de gradient intégrée a été utilisée pour expliquer la sortie du modèle. Nos résultats ont démontré une précision globale de 0,95. L'aire sous la courbe rappel précision (Precision Recall Curve, PRC-AUC) était en moyenne de 0,988 pour BD, 0,999 pour RP, 0,996 pour STGD1 et 0,989 pour les témoins sains. Une deuxième approche, utilisant la génération de données, 5 CNN différents et le t-SNE comme méthode de visualisation, a donné une précision globale de 0,982 pour distinguer les images STGD1, RP, BD et FAF saines. En conclusion, cette étude décrit pour la première fois l'utilisation d'un modèle DL pour détecter et classer automatiquement les IRD sur l'imagerie FAF.Deuxièmement, nous avons cherché à classer automatiquement l'atrophie rétinienne, à l'aide d'un modèle DL, selon son étiologie: génétique (stades tardifs des cas génétiquement confirmés de STGD1 et Pseduo-Stargardt Pattern Dystrophy (PSPD), associée à la mutation PRPH2) ou associée à la dégénérescence maculaire liée à l'âge (DMLA) atrophique. Un CNN pré-entraîné (ResNet101) et l'apprentissage par transfert de la base de données ImageNet ont été utilisés sur 314 images FAF, dont 110 images étaient des yeux GA et 204 étaient des cas avec STGD1 ou PSPD génétiquement confirmés. Les meilleures performances du modèle ont été obtenues en utilisant 10 époques, avec une précision de 0,92 et une aire sous la courbe pour la courbe Receiver Operating Characteristics (AUC-ROC) de 0,981. Il est important de faire la distinction entre l'atrophie secondaire aux IRD ou à la DMLA sèche, cela pouvant avoir un impact sur le pronostic de la maladie, la nécessité d'un conseil génétique et le taux de progression de la maladie.Troisièmement, nous avons cherché à différencier, à l'aide d'un modèle DL, entre STGD1 et PSPD, en utilisant un ensemble d'images FAF de patients avec diagnostic génétique (mutation ABCA4 pour STGD1 et mutation PRPH2 pour PSPD). Pour cela, nous avons pré-entraîné le CNN avec diverses images FAF. Le modèle a atteint une précision globale de 0,88 et une AUC-ROC de 0,89 sur l'ensemble de test, composé de 111 images. Nous avons comparé cette précision avec celle des experts rétiniens spécialisés dans les IRD, dont la précision était de 0,816, et avec la précision des chefs de clinique en rétine médicale pour distinguer les deux, avec une moyenne de 0,724. Par conséquent, le modèle DL n'était pas seulement non inférieur aux lecteurs humains, mais sa précision était supérieure.Ainsi, les classificateurs DL créés ont montré d'excellents résultats. Avec de nouveaux développements, ces modèles peuvent être des outils de diagnostic fiables et fournir des informations pertinentes pour les futures approches thérapeutiques dans les IRD.Inherited retinal diseases (IRDs) are a group of genetic diseases affecting the retina, with an estimated prevalence of 1 in 3000 people, with a total of more than 2 million people worldwide. As an in vivo metabolic mapping of the retina, fundus autofluorescence imaging (FAF) plays a key role in evaluating patients with IRDs, with several FAF phenotypes described for each IRD.This work aimed to apply deep learning (DL) to FAF in IRDs.Firstly, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD1), as well as a healthy comparable group were used to train a multi-layer deep convolutional neural network (CNN) to differentiate on FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For the subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Integrated gradient visualization was used to explain the model’s output. Our results have demonstrated a global accuracy of 0.95. Precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD1, and 0.989 for healthy controls. A second approach, using data generation, training on 5 different CNNs, and t-SNE as a visualization method yielded an overall accuracy of 0.982 in distinguishing between STGD1, RP, BD, and healthy FAF images. In conclusion, we have shown this study describes for the first time the use of a DL model to automatically detect and classify IRDs on FAF imaging.Secondly, we aimed at automatically classifying retinal atrophy, using a DL model, according to its etiology: genetic (late-stages of genetically confirmed cases of STGD1 and Pseudo-Stargardt Pattern Dystrophy (PSPD), associated with PRPH2 mutation) or associated to dry Age-related macular degeneration (AMD). A pre-trained CNN (ResNet101) and transfer learning from the ImageNet database were used on 314 FAF images, of which 110 images were of GA eyes and 204 were eyes with genetically confirmed STGD1 or PSPD. The best performance of the model was obtained using 10 epochs, with an accuracy of 0.92 and an area under the curve for Receiver Operating Characteristic (AUC-ROC) of 0.981. It is important to distinguish between atrophy secondary to IRDs or to dry AMD, as it may impact the prognosis of the disease, the need for genetic counseling, and the rate of disease progression.Thirdly, we aimed at differentiating, using a DL model, between STGD1 and PSPD, using a dataset of FAF images of patients with molecular diagnosis (ABCA4 mutation for STGD1 and PRPH2 mutation for PSPD). For this, we pretrained the CNN with various FAF images. The model achieved an overall accuracy of 0.88 and an AUC-ROC of 0.89 on the test set, consisting of 111 images. We compared this accuracy with that of retinal experts specialized in IRDs, whose accuracy was 0.816, and with retina fellows’ accuracy in distinguishing the two, averaging 0.724. Therefore, the DL model was not only non-inferior to human graders, but its accuracy was superior.Hereby, the created DL classifiers showed excellent results. With further developments, these models may be reliable diagnostic tools and give relevant information for future therapeutic approaches in IRDs

Long-Term Resolution of Perifoveal Exudative Vascular Anomalous Complex after Intravitreal Injections of Anti-Vascular Endothelial Growth Factor

Perifoveal exudative vascular anomalous complex (PEVAC) is a perifoveal aneurysmal vascular lesion found in healthy subjects. A 68-year-old woman was diagnosed with a typical unilateral and unifocal PEVAC lesion after extensive multimodal imaging and was treated with three-monthly intravitreal injections of ranibizumab. An immediate and complete resolution of the intraretinal fluid was observed. Visual acuity returned to 20/20 without any recurrence of the exudative signs along the 5 years of follow-up. Therefore, an initial anti-VEGF treatment with three-monthly intravitreal injections may be considered as a first-line treatment in PEVAC lesions and may result in long-term preservation of visual acuity

Choroidal Neovascularization Screening on OCT-Angiography Choriocapillaris Images by Convolutional Neural Networks

Choroidal Neovascularization (CNV) is the advanced stage of Age-related Macular Degeneration (AMD), which is the leading cause of irreversible visual loss for elder people in developed countries. Optical Coherence Tomography Angiography (OCTA) is a recent non-invasive imaging technique widely used nowadays in diagnosis and follow-up of CNV. In this study, an automatic screening of CNV based on deep learning is performed using OCTA choriocapillaris images. CNV eyes (advanced wet AMD) are diagnosed among healthy eyes (no AMD) and eyes with drusen (intermediate AMD). An OCTA dataset of 1396 images is used to train and evaluate the model. A pre-trained convolutional neural network (CNN) is fine-tuned and validated on 80% of the dataset while the remaining 20% is used independently for predictions. The model can accurately detect CNV on the test set with an accuracy of 89.74%, precision of 0.96 and 0.99 area under the curve of the receiver operating characteristic. A good overall classification accuracy of 88.46% is obtained on a balanced test set. Detailed analysis of misclassified images shows that they are also considered ambiguous images for expert clinicians. This novel CNN-based application is truly a breakthrough to assist clinicians in the challenging task of screening for neovascular complications

Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy

Abstract To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigment epithelium atrophy secondary to either EMAP (EMAP Group) or to dry age related macular degeneration (AMD group) were retrospectively selected. Fovea-centered posterior pole (30° × 30°) and 55° × 55° degree-field-of-view FAF images of sufficiently high quality were collected and used to train two different deep learning (DL) classifiers based on ResNet-101 design. Testing was performed on a set of images coming from a different center. A total of 300 patients were recruited, 135 belonging to EMAP group and 165 belonging to AMD group. The 30° × 30° FAF based DL classifier showed a sensitivity of 84.6% and a specificity of 85.3% for the diagnosis of EMAP. The 55° × 55° FAF based DL classifier showed a sensitivity of 90% and a specificity of 84.6%, a performance that was significantly higher than that of the 30° × 30° classifer (p = 0.037). Artificial intelligence can accurately distinguish between atrophy caused by AMD or by EMAP on FAF images. Its performance are improved using wide field acquisitions

Automated quantification of choroidal neovascularization on Optical Coherence Tomography Angiography images

International audienc

Type 1 Idiopathic Macular Telangiectasia Associated with Type 3 Neovascularization

Purpose: To report the case of a patient with unilateral idiopathic macular telangiectasia (IMT) associated with type 3 neovascularization. Methods: Observational case report. Results: We describe a case of an 85-year-old woman who presented at our department with a gradual vision loss in her left eye (LE). Her best-corrected visual acuity (BCVA) was 20/200 in the LE. Fundus examination showed 2 small hemorrhages located nasally to the LE fovea, as well as lipid exudates. Fluorescein angiography revealed early hyperfluorescence corresponding to the dilated capillaries. Spectral-domain optical coherence tomography (SD-OCT) showed several microaneurysms within the inner retinal layers. Late indocyanine green angiography revealed a focal hyperfluorescence corresponding to a type 3 neovascularization. No signs of IMT or type 3 neovascularization were detected in the right eye. Based on these findings, the patient was diagnosed with type 1 IMT and coincident type 3 neovascularization. The LE was treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections. Twenty-four months later, SD-OCT revealed regression of the exudative signs, and LE BCVA improved to 20/100. Conclusion: We describe the case of an unusual association between older-onset IMT and type 3 neovascularization, and subsequent regression by anti-VEGF injections. We propose a new IMT subtype called type 1C for this association. Further research must be done in order to establish the pathophysiologic mechanism and likelihood of this association