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Curcio CA, Kar D, Owsley C, Sloan KR, Ach T. Age-Related Macular Degeneration, a Mathematically Tractable Disease. Invest Ophthalmol Vis Sci 2024; 65:4. [PMID: 38466281 PMCID: PMC10916886 DOI: 10.1167/iovs.65.3.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/19/2024] [Indexed: 03/12/2024] Open
Abstract
A progression sequence for age-related macular degeneration onset may be determinable with consensus neuroanatomical nomenclature augmented by drusen biology and eye-tracked clinical imaging. This narrative review proposes to supplement the Early Treatment of Diabetic Retinopathy Study (sETDRS) grid with a ring to capture high rod densities. Published photoreceptor and retinal pigment epithelium (RPE) densities in flat mounted aged-normal donor eyes were recomputed for sETDRS rings including near-periphery rich in rods and cumulatively for circular fovea-centered regions. Literature was reviewed for tissue-level studies of aging outer retina, population-level epidemiology studies regionally assessing risk, vision studies regionally assessing rod-mediated dark adaptation (RMDA), and impact of atrophy on photopic visual acuity. The 3 mm-diameter xanthophyll-rich macula lutea is rod-dominant and loses rods in aging whereas cone and RPE numbers are relatively stable. Across layers, the largest aging effects are accumulation of lipids prominent in drusen, loss of choriocapillary coverage of Bruch's membrane, and loss of rods. Epidemiology shows maximal risk for drusen-related progression in the central subfield with only one third of this risk level in the inner ring. RMDA studies report greatest slowing at the perimeter of this high-risk area. Vision declines precipitously when the cone-rich central subfield is invaded by geographic atrophy. Lifelong sustenance of foveal cone vision within the macula lutea leads to vulnerability in late adulthood that especially impacts rods at its perimeter. Adherence to an sETDRS grid and outer retinal cell populations within it will help dissect mechanisms, prioritize research, and assist in selecting patients for emerging treatments.
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Affiliation(s)
- Christine A. Curcio
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, United States
| | - Deepayan Kar
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, United States
| | - Cynthia Owsley
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, United States
| | - Kenneth R. Sloan
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, United States
| | - Thomas Ach
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
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Cheung R, Trinh M, Tee YG, Nivison-Smith L. RPE Curvature Can Screen for Early and Intermediate AMD. Invest Ophthalmol Vis Sci 2024; 65:2. [PMID: 38300558 PMCID: PMC10846343 DOI: 10.1167/iovs.65.2.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/09/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose Diagnosing AMD early optimizes clinical management. However, current diagnostic accuracy is limited by the subjectivity of qualitative diagnostic measures used in clinical practice. This study tests if RPE curvature could be an accurate, quantitative measure for AMD diagnosis. Methods Consecutive patients without AMD or normal aging changes (n = 111), with normal aging changes (n = 107), early AMD (n = 102) and intermediate AMD (n = 114) were recruited. RPE curvature was calculated based on the sinuosity method of measuring river curvature in environmental science. RPE and Bruch's membrane were manually segmented from optical coherence tomography B-scans and then their lengths automatically extracted using customized MATLAB code. RPE sinuosity was calculated as a ratio of RPE to Bruch's membrane length. Diagnostic accuracy was determined from area under the receiver operator characteristic curve (aROC). Results RPE sinuosity of foveal B-scans could distinguish any eyes with AMD (early or intermediate) from those without AMD (non-AMD or eyes with normal aging changes) with acceptable diagnostic accuracy (aROC = 0.775). Similarly, RPE sinuosity could identify intermediate AMD from all other groups (aROC = 0.871) and distinguish between early and intermediate AMD (aROC = 0.737). RPE sinuosity was significantly associated with known AMD lesions: reticular pseudodrusen (P < 0.0001) and drusen volume (P < 0.0001), but not physiological variables such as age, sex, and ethnicity. Conclusions RPE sinuosity is a simple, robust, quantitative biomarker that is amenable to automation and could enhance screening of AMD.
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Affiliation(s)
- Rene Cheung
- School of Optometry and Vision Science, University of New South Wales, Sydney, Australia
- Centre for Eye Health, University of New South Wales, Sydney, Australia
| | - Matt Trinh
- School of Optometry and Vision Science, University of New South Wales, Sydney, Australia
- Centre for Eye Health, University of New South Wales, Sydney, Australia
| | - Yoh Ghen Tee
- School of Optometry and Vision Science, University of New South Wales, Sydney, Australia
- Centre for Eye Health, University of New South Wales, Sydney, Australia
| | - Lisa Nivison-Smith
- School of Optometry and Vision Science, University of New South Wales, Sydney, Australia
- Centre for Eye Health, University of New South Wales, Sydney, Australia
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Riazi Esfahani P, Reddy AJ, Nawathey N, Ghauri MS, Min M, Wagh H, Tak N, Patel R. Deep Learning Classification of Drusen, Choroidal Neovascularization, and Diabetic Macular Edema in Optical Coherence Tomography (OCT) Images. Cureus 2023; 15:e41615. [PMID: 37565126 PMCID: PMC10411652 DOI: 10.7759/cureus.41615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2023] [Indexed: 08/12/2023] Open
Abstract
Background Age-related macular degeneration (AMD), diabetic retinopathy (DR), drusen, choroidal neovascularization (CNV), and diabetic macular edema (DME) are significant causes of visual impairment globally. Optical coherence tomography (OCT) imaging has emerged as a valuable diagnostic tool for these ocular conditions. However, subjective interpretation and inter-observer variability highlight the need for standardized diagnostic approaches. Methods This study aimed to develop a robust deep learning model using artificial intelligence (AI) techniques for the automated detection of drusen, CNV, and DME in OCT images. A diverse dataset of 1,528 OCT images from Kaggle.com was used for model training. The performance metrics, including precision, recall, sensitivity, specificity, F1 score, and overall accuracy, were assessed to evaluate the model's effectiveness. Results The developed model achieved high precision (0.99), recall (0.962), sensitivity (0.985), specificity (0.987), F1 score (0.971), and overall accuracy (0.987) in classifying diseased and healthy OCT images. These results demonstrate the efficacy and efficiency of the model in distinguishing between retinal pathologies. Conclusion The study concludes that the developed deep learning model using AI techniques is highly effective in the automated detection of drusen, CNV, and DME in OCT images. Further validation studies and research efforts are necessary to evaluate the generalizability and integration of the model into clinical practice. Collaboration between clinicians, policymakers, and researchers is essential for advancing diagnostic tools and management strategies for AMD and DR. Integrating this technology into clinical workflows can positively impact patient care, particularly in settings with limited access to ophthalmologists. Future research should focus on collecting independent datasets, addressing potential biases, and assessing real-world effectiveness. Overall, the use of machine learning algorithms in conjunction with OCT imaging holds great potential for improving the detection and management of drusen, CNV, and DME, leading to enhanced patient outcomes and vision preservation.
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Affiliation(s)
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | - Neel Nawathey
- Ophthalmology, California Northstate University, Rancho Cordova, USA
| | - Muhammad S Ghauri
- Neurosurgery, California University of Science and Medicine, Colton, USA
| | - Mildred Min
- Dermatology, California Northstate University College of Medicine, Elk Grove, USA
| | - Himanshu Wagh
- Medicine, California Northstate University College of Medicine, Elk Grove, USA
| | - Nathaniel Tak
- Medicine, Arizona College of Osteopathic Medicine, Midwestern University, Glendale, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University, Quillen College of Medicine, Johnson City, USA
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Pavithra K, Kumar P, Geetha M, Bhandary SV. Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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DeepPDT-Net: predicting the outcome of photodynamic therapy for chronic central serous chorioretinopathy using two-stage multimodal transfer learning. Sci Rep 2022; 12:18689. [PMID: 36333442 PMCID: PMC9636239 DOI: 10.1038/s41598-022-22984-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
Central serous chorioretinopathy (CSC), characterized by serous detachment of the macular retina, can cause permanent vision loss in the chronic course. Chronic CSC is generally treated with photodynamic therapy (PDT), which is costly and quite invasive, and the results are unpredictable. In a retrospective case-control study design, we developed a two-stage deep learning model to predict 1-year outcome of PDT using initial multimodal clinical data. The training dataset included 166 eyes with chronic CSC and an additional learning dataset containing 745 healthy control eyes. A pre-trained ResNet50-based convolutional neural network was first trained with normal fundus photographs (FPs) to detect CSC and then adapted to predict CSC treatability through transfer learning. The domain-specific ResNet50 successfully predicted treatable and refractory CSC (accuracy, 83.9%). Then other multimodal clinical data were integrated with the FP deep features using XGBoost.The final combined model (DeepPDT-Net) outperformed the domain-specific ResNet50 (accuracy, 88.0%). The FP deep features had the greatest impact on DeepPDT-Net performance, followed by central foveal thickness and age. In conclusion, DeepPDT-Net could solve the PDT outcome prediction task challenging even to retinal specialists. This two-stage strategy, adopting transfer learning and concatenating multimodal data, can overcome the clinical prediction obstacles arising from insufficient datasets.
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Zhang M, Gong X, Ma W, Wen L, Wang Y, Yao H. A Study on the Correlation Between Age-Related Macular Degeneration and Alzheimer's Disease Based on the Application of Artificial Neural Network. Front Public Health 2022; 10:925147. [PMID: 35844883 PMCID: PMC9280183 DOI: 10.3389/fpubh.2022.925147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Age-related Macular Degeneration (AMD) is a kind of irreversible vision loss or disease caused by retinal pigment epithelial cells and neuroretinal degeneration, which has become the main cause of vision loss and blindness of the elderly over 65 years old in developed countries. The main clinical manifestations are cognitive decline, mental symptoms and behavioral disorders, and the gradual decline of daily living ability. In this paper, a feature extraction method of electroencephalogram (EEG) signal based on multi-spectral image fusion of multi-brain regions is proposed based on artificial neural network (ANN). In this method, the brain is divided into several different brain regions, and the EEG signals of different brain regions are transformed into several multispectral images by combining with the multispectral image transformation method. Using Alzheimer's disease (AD) classification algorithm, the depth residual network model pre-trained in ImageNet was transferred to sMRI data set for fine adjustment, instead of training a brand-new model from scratch. The results show that the proposed method solves the problem of few available medical image samples and shortens the training time of ANN model.
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Affiliation(s)
- Meng Zhang
- Histology and Embryology Section, Qiqihar Medical University, Qiqihar, China
| | - Xuewu Gong
- The Second Affiliated Hospital of Qiqihar Medical University, Qiqihar, China
| | - Wenhui Ma
- Computer Experimental Teaching Center, Qiqihar Medical University, Qiqihar, China
| | - Libo Wen
- Physiology Section, Qiqihar Medical University, Qiqihar, China
| | - Yuejing Wang
- Histology and Embryology Section, Qiqihar Medical University, Qiqihar, China
| | - Hongbo Yao
- Histology and Embryology Section, Qiqihar Medical University, Qiqihar, China
- *Correspondence: Hongbo Yao
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Toğaçar M, Ergen B, Tümen V. Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Ma D, Kumar M, Khetan V, Sen P, Bhende M, Chen S, Yu TTL, Lee S, Navajas EV, Matsubara JA, Ju MJ, Sarunic MV, Raman R, Beg MF. Clinical explainable differential diagnosis of polypoidal choroidal vasculopathy and age-related macular degeneration using deep learning. Comput Biol Med 2022; 143:105319. [PMID: 35220077 DOI: 10.1016/j.compbiomed.2022.105319] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND This study aims to achieve an automatic differential diagnosis between two types of retinal pathologies with similar pathological features - Polypoidal choroidal vasculopathy (PCV) and wet age-related macular degeneration (AMD) from volumetric optical coherence tomography (OCT) images, and identify clinically-relevant pathological features, using an explainable deep-learning-based framework. METHODS This is a retrospective study with data from a cross-sectional cohort. The OCT volume of 73 eyes from 59 patients was included in this study. Disease differentiation was achieved through single-B-scan-based classification followed by a volumetric probability prediction aggregation step. We compared different labeling strategies with and without identifying pathological B-scans within each OCT volume. Clinical interpretability was achieved through normalized aggregation of B-scan-based saliency maps followed by maximum-intensity-projection onto the en face plane. We derived the PCV score from the proposed differential diagnosis framework with different labeling strategies. The en face projection of saliency map was validated with the pathologies identified in Indocyanine green angiography (ICGA). RESULTS Model trained with both labeling strategies achieved similar level differentiation power (>90%), with good correspondence between pathological features detected from the projected en face saliency map and ICGA. CONCLUSIONS This study demonstrated the potential clinical application of non-invasive differential diagnosis using AI-driven OCT-based analysis, with minimal requirement of labeling efforts, along with clinical explainability achieved through automatically detected disease-related pathologies.
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Affiliation(s)
- Da Ma
- Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA; School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
| | - Meenakshi Kumar
- Shri Bhagwan Mahavir Vitreoretinal Service, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Vikas Khetan
- Shri Bhagwan Mahavir Vitreoretinal Service, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Parveen Sen
- Shri Bhagwan Mahavir Vitreoretinal Service, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Muna Bhende
- Shri Bhagwan Mahavir Vitreoretinal Service, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Shuo Chen
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Timothy T L Yu
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Sieun Lee
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Eduardo V Navajas
- Department of Ophthalmology & Visual Sciences, The University of British Columbia, Vancouver, BC, Canada; University of British Columbia Vancouver General Hospital, Eye Care Centre, Vancouver, BC, Canada
| | - Joanne A Matsubara
- Department of Ophthalmology & Visual Sciences, The University of British Columbia, Vancouver, BC, Canada; University of British Columbia Vancouver General Hospital, Eye Care Centre, Vancouver, BC, Canada
| | - Myeong Jin Ju
- Department of Ophthalmology & Visual Sciences, The University of British Columbia, Vancouver, BC, Canada; University of British Columbia Vancouver General Hospital, Eye Care Centre, Vancouver, BC, Canada; School of Biomedical Engineering, University of British Columbia, BC, Canada
| | - Marinko V Sarunic
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Institute of Ophthalmology, University College London, London, UK; Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Service, Medical Research Foundation, Sankara Nethralaya, Chennai, India.
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
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Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12030742. [PMID: 35328292 PMCID: PMC8947335 DOI: 10.3390/diagnostics12030742] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/10/2022] [Accepted: 03/16/2022] [Indexed: 12/20/2022] Open
Abstract
Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary’s Hospital and Seoul St. Mary’s Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.
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Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
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Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
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