{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:38:53Z","timestamp":1793162333630,"version":"4.5.0"},"reference-count":110,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the \u201cblack box phenomenon\u201d, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.<\/jats:p>","DOI":"10.3390\/medicina60040527","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:47:11Z","timestamp":1711370831000},"page":"527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases"],"prefix":"10.3390","volume":"60","author":[{"given":"Uday Pratap Singh","family":"Parmar","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Government Medical College and Hospital, Chandigarh 160030, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7721-4694","authenticated-orcid":false,"given":"Pier Luigi","family":"Surico","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA"},{"name":"Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy"},{"name":"Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2426-3900","authenticated-orcid":false,"given":"Rohan Bir","family":"Singh","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Romano","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Salati","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leopoldo","family":"Spadea","sequence":"additional","affiliation":[{"name":"Eye Clinic, Policlinico Umberto I, \u201cSapienza\u201d University of Rome, 00142 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7486-8361","authenticated-orcid":false,"given":"Mutali","family":"Musa","sequence":"additional","affiliation":[{"name":"Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0068","authenticated-orcid":false,"given":"Caterina","family":"Gagliano","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Surgery, University of Enna \u201cKore\u201d, Piazza dell\u2019Universit\u00e0, 94100 Enna, Italy"},{"name":"Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Mori","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy"},{"name":"Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"},{"name":"Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0999-5545","authenticated-orcid":false,"given":"Marco","family":"Zeppieri","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"ref_1","unstructured":"(2024, February 23). The Fourth Industrial Revolution: What It Means and How to Respond|World Economic Forum. Available online: https:\/\/www.weforum.org\/agenda\/2016\/01\/the-fourth-industrial-revolution-what-it-means-and-how-to-respond\/."},{"key":"ref_2","unstructured":"Koza, J.R., Bennett, F.H., Andre, D., and Keane, M.A. (1996). Artificial Intelligence in Design \u201996, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1111\/cas.14377","article-title":"Artificial intelligence in oncology","volume":"111","author":"Shimizu","year":"2020","journal-title":"Cancer Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2374289519873088","DOI":"10.1177\/2374289519873088","article-title":"Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods","volume":"6","author":"Rashidi","year":"2019","journal-title":"Acad. Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Obuchowski, N.A., and Bullen, J.A. (2018). Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine. Phys. Med. Biol., 63.","DOI":"10.1088\/1361-6560\/aab4b1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1080\/08820538.2023.2168486","article-title":"Bias and Non-Diversity of Big Data in Artificial Intelligence: Focus on Retinal Diseases","volume":"38","author":"Jacoba","year":"2023","journal-title":"Semin. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1016\/S2589-7500(19)30217-1","article-title":"Artificial intelligence, the internet of things, and virtual clinics: Ophthalmology at the digital translation forefront","volume":"2","author":"Ting","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","article-title":"Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045","volume":"128","author":"Teo","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"P66","DOI":"10.1016\/j.ophtha.2019.09.025","article-title":"Diabetic Retinopathy Preferred Practice Pattern\u00ae","volume":"127","author":"Flaxel","year":"2020","journal-title":"Ophthalmology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.cmpb.2018.02.016","article-title":"Microaneurysm detection using fully convolutional neural networks","volume":"158","author":"Chudzik","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cmpb.2017.10.017","article-title":"An ensemble deep learning based approach for red lesion detection in fundus images","volume":"153","author":"Orlando","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yan, Y., Gong, J., and Liu, Y. (2019, January 3\u20135). A Novel Deep Learning Method for Red Lesions Detection Using Hybrid Feature. Proceedings of the 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China.","DOI":"10.1109\/CCDC.2019.8833190"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.eswa.2018.07.053","article-title":"Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks","volume":"114","author":"Adem","year":"2018","journal-title":"Expert. Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, H., Yuan, G., Zhao, X., Peng, L., Wang, Z., He, Y., Qu, C., and Peng, Z. (2020). Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening. Comput. Methods Programs Biomed., 191.","DOI":"10.1016\/j.cmpb.2020.105398"},{"key":"ref_15","unstructured":"(2024, February 22). FDA Permits Marketing of Artificial Intelligence-Based Device to Detect Certain Diabetes-Related Eye Problems|FDA, Available online: https:\/\/www.fda.gov\/news-events\/press-announcements\/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Lavin","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1001\/jamaophthalmol.2013.1743","article-title":"Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy","volume":"131","author":"Folk","year":"2013","journal-title":"JAMA Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset through Integration of Deep Learning","volume":"57","author":"Lou","year":"2016","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1159\/000368426","article-title":"Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated \u2018Disease\/No Disease\u201d Grading\u2019","volume":"233","author":"Ribeiro","year":"2015","journal-title":"Ophthalmologica"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","article-title":"Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence","volume":"32","author":"Rajalakshmi","year":"2018","journal-title":"Eye"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.2337\/dc12-1491","article-title":"Microaneurysm Turnover at the Macula Predicts Risk of Development of Clinically Significant Macular Edema in Persons with Mild Nonproliferative Diabetic Retinopathy","volume":"36","author":"Ribeiro","year":"2013","journal-title":"Diabetes Care"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1097\/IAE.0b013e318295f6de","article-title":"Microaneurysm formation rate as a predictive marker for progression to clinically significant macular edema in nonproliferative diabetic retinopathy","volume":"34","author":"Haritoglou","year":"2014","journal-title":"Retina"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3310\/hta20920","article-title":"An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness","volume":"20","author":"Tufail","year":"2016","journal-title":"Health Technol. Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ophtha.2016.11.014","article-title":"Automated Diabetic Retinopathy Image Assessment Software","volume":"124","author":"Tufail","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Abdulrazak, L.F., Nahiduzzaman, M., Goni, M.O.F., Anower, M.S., Ahsan, M., Haider, J., and Kowalski, M. (2022). Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105602"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Elgafi, M., Sharafeldeen, A., Elnakib, A., Elgarayhi, A., Alghamdi, N.S., Sallah, M., and El-Baz, A. (2022). Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images. Sensors, 22.","DOI":"10.3390\/s22207833"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1730501","DOI":"10.1155\/2022\/1730501","article-title":"Hypermixed Convolutional Neural Network for Retinal Vein Occlusion Classification","volume":"2022","author":"Zhang","year":"2022","journal-title":"Dis. Markers"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.artmed.2019.03.009","article-title":"A data-driven approach to referable diabetic retinopathy detection","volume":"96","author":"Pires","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., and Qian, W. (2019, January 23\u201327). An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857160"},{"key":"ref_31","first-page":"233","article-title":"Leonardo Electronic Journal of Practices and Technologies Classification of diabetic and normal fundus images using new deep learning method","volume":"17","author":"Esfahani","year":"2018","journal-title":"Leonardo Electron. J. Pract. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional Neural Networks for Diabetic Retinopathy","volume":"90","author":"Pratt","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"839088","DOI":"10.3389\/fmed.2022.839088","article-title":"The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy","volume":"9","author":"Zhang","year":"2022","journal-title":"Front. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1177\/19322968211042665","article-title":"Evaluation of a New Neural Network Classifier for Diabetic Retinopathy","volume":"16","author":"Katz","year":"2022","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","article-title":"Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis","volume":"2","author":"Wong","year":"2014","journal-title":"Lancet Glob. Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.ophtha.2020.11.024","article-title":"Genetic Risk, Lifestyle, and Age-Related Macular Degeneration in Europe","volume":"128","author":"Colijn","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.amepre.2012.02.028","article-title":"Diabetic Retinopathy and Age-Related Macular Degeneration in the U.S","volume":"43","author":"Gibson","year":"2012","journal-title":"Am. J. Prev. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.compbiomed.2015.05.019","article-title":"Local configuration pattern features for age-related macular degeneration characterization and classification","volume":"63","author":"Mookiah","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1167\/iovs.12-11449","article-title":"Automatic Drusen Quantification and Risk Assessment of Age-Related Macular Degeneration on Color Fundus Images","volume":"54","author":"Lechanteur","year":"2013","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compbiomed.2017.01.018","article-title":"Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis","volume":"82","author":"Burlina","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7130","DOI":"10.1038\/s41598-021-86526-2","article-title":"Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration","volume":"11","author":"Chou","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1001\/jamaophthalmol.2020.1376","article-title":"Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography","volume":"138","author":"Waldstein","year":"2020","journal-title":"JAMA Ophthalmol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s42256-020-0154-9","article-title":"Deep-learning-based prediction of late age-related macular degeneration progression","volume":"2","author":"Yan","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","article-title":"Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning","volume":"125","author":"Schlegl","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1097\/IAE.0000000000003683","article-title":"Natural course and classification of extensive macular atrophy with pseudodrusen-like appearance","volume":"43","author":"Romano","year":"2023","journal-title":"Retina"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100016","DOI":"10.1016\/j.xops.2021.100016","article-title":"Progression of Atrophy and Visual Outcomes in Extensive Macular Atrophy with Pseudodrusen-like Appearance","volume":"1","author":"Romano","year":"2021","journal-title":"Ophthalmol. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.ajo.2008.10.022","article-title":"Extensive macular atrophy with pseudodrusen-like appearance: A new clinical entity","volume":"147","author":"Hamel","year":"2009","journal-title":"Am. J. Ophthalmol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"20354","DOI":"10.1038\/s41598-023-47854-7","article-title":"Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy","volume":"13","author":"Chouraqui","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1167\/tvst.9.2.25","article-title":"Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD","volume":"9","author":"Bhuiyan","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15434","DOI":"10.1038\/s41598-020-72359-y","article-title":"Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers","volume":"10","author":"Banerjee","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1167\/iovs.18-24106","article-title":"Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence","volume":"59","author":"Waldstein","year":"2018","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","article-title":"Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images","volume":"1","author":"Lee","year":"2017","journal-title":"Ophthalmol. Retina"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.oret.2017.03.015","article-title":"Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration","volume":"2","author":"Bogunovic","year":"2018","journal-title":"Ophthalmol. Retina"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1038\/s41598-022-05903-7","article-title":"Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images","volume":"12","author":"Han","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"952735","DOI":"10.3389\/fnins.2022.952735","article-title":"Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning","volume":"16","author":"Song","year":"2022","journal-title":"Front. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1109\/JBHI.2020.3000136","article-title":"End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD from Longitudinal Retinal OCT Imaging","volume":"24","author":"Erfurth","year":"2020","journal-title":"IEEE J. Biomed Health Inform."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"e27822","DOI":"10.2196\/27822","article-title":"Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study","volume":"23","author":"Han","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Cho, S.W., and Park, K.R. (2019). Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation. J. Clin. Med., 8.","DOI":"10.3390\/jcm8091446"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"794045","DOI":"10.3389\/fmed.2022.794045","article-title":"Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients with Branch Retinal Vein Occlusion","volume":"9","author":"Miao","year":"2022","journal-title":"Front. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e2217447","DOI":"10.1001\/jamanetworkopen.2022.17447","article-title":"Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity","volume":"5","author":"Wu","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.ebiom.2018.08.033","article-title":"Automated retinopathy of prematurity screening using deep neural networks","volume":"35","author":"Wang","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Alam, M., Thapa, D., Lim, J.I., Cao, D., and Yao, X. (2017). Computer-aided classification of sickle cell retinopathy using quantitative features in optical coherence tomography angiography. Biomed. Opt. Express, 8.","DOI":"10.1364\/BOE.8.004206"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Early Treatment for Retinopathy of Prematurity Cooperative Group (2005). The Incidence and Course of Retinopathy of Prematurity: Findings from the Early Treatment for Retinopathy of Prematurity Study. Pediatrics, 116, 15\u201323.","DOI":"10.1542\/peds.2004-1413"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.clp.2013.02.001","article-title":"Retinopathy of prematurity-incidence today","volume":"40","author":"Zin","year":"2013","journal-title":"Clin. Perinatol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"101208","DOI":"10.1016\/j.preteyeres.2023.101208","article-title":"Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence","volume":"97","author":"Hoyek","year":"2023","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1136\/bjophthalmol-2018-313156","article-title":"Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity","volume":"103","author":"Redd","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"010427","DOI":"10.7189\/jogh.09.010427","article-title":"Global epidemiology of retinal vein occlusion: A systematic review and meta-analysis of prevalence, incidence, and risk factors","volume":"9","author":"Song","year":"2019","journal-title":"J. Glob. Health"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1097\/APO.0000000000000598","article-title":"Update on Retinal Vein Occlusion","volume":"12","author":"Romano","year":"2023","journal-title":"Asia Pac. J. Ophthalmol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Suzani, M., and Moore, A.T. (2012). MOOREA Coats disease: Fluorescein angiography guided management. Acta Ophthalmol., 90.","DOI":"10.1111\/j.1755-3768.2012.2615.x"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s00500-022-06752-2","article-title":"MicroNet: Microaneurysm detection in retinal fundus images using convolutional neural network","volume":"26","author":"Murugan","year":"2022","journal-title":"Soft Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.oret.2020.12.013","article-title":"Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity","volume":"5","author":"Chen","year":"2021","journal-title":"Ophthalmol. Retina"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e000924","DOI":"10.1136\/bmjophth-2021-000924","article-title":"Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs","volume":"7","author":"Abitbol","year":"2022","journal-title":"BMJ Open Ophthalmol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Nagasato, D., Tabuchi, H., Masumoto, H., Enno, H., Ishitobi, N., Kameoka, M., Niki, M., and Mitamura, Y. (2019). Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0223965"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e28868","DOI":"10.2196\/28868","article-title":"A Multimodal Imaging\u2013Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study","volume":"9","author":"Kang","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e935135-1","DOI":"10.12659\/MSM.935135","article-title":"Impact of Arterial Hypertension on the Eye: A Review of the Pathogenesis, Diagnostic Methods, and Treatment of Hypertensive Retinopathy","volume":"28","author":"Dziedziak","year":"2022","journal-title":"Med. Sci. Monit."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1056\/NEJMra032865","article-title":"Hypertensive Retinopathy","volume":"351","author":"Wong","year":"2004","journal-title":"N. Engl. J. Med."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2147\/OPTO.S183492","article-title":"A Review of Hypertensive Retinopathy and Chorioretinopathy","volume":"12","author":"Tsukikawa","year":"2020","journal-title":"Clin. Optom."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e229960","DOI":"10.1001\/jamanetworkopen.2022.9960","article-title":"Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases","volume":"5","author":"Dong","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.cmpb.2017.11.014","article-title":"Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy","volume":"154","author":"Akbar","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Qureshi, I., and Ibrahim, M.E.A. (2021). An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture. Sensors, 21.","DOI":"10.3390\/s21206936"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0039-6257(88)90085-9","article-title":"Retinitis pigmentosa","volume":"33","author":"Pagon","year":"1988","journal-title":"Surv. Ophthalmol."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Vingolo, E.M., Mascolo, S., Miccich\u00e8, F., and Manco, G. (2024). Retinitis Pigmentosa: From Pathomolecular Mechanisms to Therapeutic Strategies. Medicina, 60.","DOI":"10.3390\/medicina60010189"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1007\/s10278-021-00479-6","article-title":"Artificial Intelligence\u2013Assisted Early Detection of Retinitis Pigmentosa\u2014the Most Common Inherited Retinal Degeneration","volume":"34","author":"Chen","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1001\/jamaophthalmol.2022.6393","article-title":"Estimation of Visual Function Using Deep Learning from Ultra-Widefield Fundus Images of Eyes with Retinitis Pigmentosa","volume":"141","author":"Nagasato","year":"2023","journal-title":"JAMA Ophthalmol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1136\/bjo-2021-320897","article-title":"Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images","volume":"107","author":"Liu","year":"2023","journal-title":"Br. J. Ophthalmol."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Baek, N.R., Owais, M., Mahmood, T., and Park, K.R. (2020). Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa. Sensors, 20.","DOI":"10.3390\/s20123454"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.oret.2017.03.001","article-title":"The Epidemiology of Stargardt Disease in the United Kingdom","volume":"1","author":"Ho","year":"2017","journal-title":"Ophthalmol. Retina"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"100861","DOI":"10.1016\/j.preteyeres.2020.100861","article-title":"Clinical spectrum, genetic complexity and therapeutic approaches for retinal disease caused by ABCA4 mutations","volume":"79","author":"Cremers","year":"2020","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/ng0397-236","article-title":"A photoreceptor cell-specific ATP-binding transporter gene (ABCR) is mutated in recessive Stargardt macular dystrophy","volume":"15","author":"Allikmets","year":"1997","journal-title":"Nat. Genet."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Romano, F., Lamanna, F., Boon, C.J.F., Siligato, A., Kalra, G., Agarwal, A., Medori, C., Bertelli, M., Pellegrini, M., and Invernizzi, A. (2023). Clinical, Genotypic, and Imaging Characterization of the Spectrum of ABCA4 Retinopathies. Ophthalmol. Retina, in press.","DOI":"10.1016\/j.oret.2023.10.023"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"14565","DOI":"10.1038\/s41598-022-18785-6","article-title":"Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks","volume":"12","author":"Wang","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1167\/tvst.10.4.24","article-title":"Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization via Deep Learning","volume":"10","author":"Mishra","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.preteyeres.2017.01.006","article-title":"Bestrophin 1 and retinal disease","volume":"58","author":"Johnson","year":"2017","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1177\/11206721231166434","article-title":"Multimodal imaging in Best Vitelliform Macular Dystrophy: Literature review and novel insights","volume":"34","author":"Bianco","year":"2024","journal-title":"Eur. J. Ophthalmol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ajo.2017.12.013","article-title":"Retinal Vascular Impairment in Best Vitelliform Macular Dystrophy Assessed by Means of Optical Coherence Tomography Angiography","volume":"187","author":"Romano","year":"2018","journal-title":"Am. J. Ophthalmol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1097\/IAE.0000000000001634","article-title":"Spectral domain optical coherence tomography features in different stages of best vitelliform macular dystrophy","volume":"38","author":"Parodi","year":"2018","journal-title":"Retina"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.ajo.2014.07.026","article-title":"Fundus autofluorescence patterns in Best vitelliform macular dystrophy","volume":"158","author":"Parodi","year":"2014","journal-title":"Am. J. Ophthalmol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1167\/iovs.19-26571","article-title":"Multimodal Imaging in Best Vitelliform Macular Dystrophy","volume":"60","author":"Paavo","year":"2019","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"12745","DOI":"10.1038\/s41598-022-16980-z","article-title":"Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD)","volume":"12","author":"Crincoli","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"021103","DOI":"10.7189\/jogh.08.021103","article-title":"The global burden of sickle cell disease in children under five years of age: A systematic review and meta-analysis","volume":"8","author":"Wastnedge","year":"2018","journal-title":"J. Glob. Health"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1097\/ICU.0b013e328358b921","article-title":"Ophthalmic manifestations of sickle cell disease","volume":"23","author":"Lim","year":"2012","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.oret.2023.09.005","article-title":"Longitudinal Assessment of Macular Thickness and Microvascular Changes in Children with Sickle Cell Disease","volume":"8","author":"Hoyek","year":"2024","journal-title":"Ophthalmol. Retina"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/0002-9394(71)90429-6","article-title":"Classification and Pathogenesis of Proliferative Sickle Retinopathy","volume":"71","author":"Goldberg","year":"1971","journal-title":"Am. J. Ophthalmol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1001\/jama.2014.10517","article-title":"Management of Sickle Cell Disease","volume":"312","author":"Yawn","year":"2014","journal-title":"JAMA"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1001\/jamaophthalmol.2020.5900","article-title":"Deep Learning Detection of Sea Fan Neovascularization from Ultra-Widefield Color Fundus Photographs of Patients with Sickle Cell Hemoglobinopathy","volume":"139","author":"Cai","year":"2021","journal-title":"JAMA Ophthalmol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1136\/bjophthalmol-2020-317241","article-title":"Longitudinal assessment of quantitative ultra-widefield ischaemic and vascular parameters in sickle cell retinopathy","volume":"106","author":"Sevgi","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1001\/jama.2016.17563","article-title":"Artificial Intelligence with Deep Learning Technology Looks into Diabetic Retinopathy Screening","volume":"316","author":"Wong","year":"2016","journal-title":"JAMA"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Froomkin, A.M., Kerr, I.R., and Pineau, J. (2018). When AIs Outperform Doctors: The Dangers of a Tort-Induced Over-Reliance on Machine Learning and What (Not) to Do about it. SSRN Electron. J., 18-3.","DOI":"10.2139\/ssrn.3114347"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_110","unstructured":"(2024, February 22). Teleophthalmology: Ready for Prime Time?\u2014American Academy of Ophthalmology. Available online: https:\/\/www.aao.org\/eyenet\/article\/teleophthalmology-ready-prime-time."}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/60\/4\/527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:17:33Z","timestamp":1760105853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/60\/4\/527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,23]]},"references-count":110,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["medicina60040527"],"URL":"https:\/\/doi.org\/10.3390\/medicina60040527","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,23]]}}}