International journal of general medicine · 2025
Practical Applications of Artificial Intelligence Diagnostic Systems in Fundus Retinal Disease Screening.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bhattacharya Romit, Uddin Md Mesbah, Patel Aniruddh P, Niroula Abhishek, Finneran Phoebe, Bernardo Rachel, Fitch Kathleen V, Lu Michael T, Bloomfield Gerald S, Malvestutto Carlos, Aberg Judy A, Fichtenbaum Carl J, Hornsby Whitney, Ribaudo Heather J, Libby Peter, Ebert Benjamin L, Zanni Markella V, Douglas Pamela S, Grinspoon Steven K, Natarajan Pradeep
Аннотация
PURPOSE: This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications.
METHODS: A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.
RESULTS: : The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.
CONCLUSIONS: : The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.
METHODS: A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.
RESULTS: : The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.
CONCLUSIONS: : The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.