Therefore, early detection at the initial stages and positive screening at the pre-hospital level were necessary, indicating the importance of effective and efficient community screening. Among them, the age-related macular degeneration (AMD), diabetic retinopathy (DR), and myopic retinopathy, as well as other macular disorders like epiretinal membranes (ERMs) and macular holes, were the significant components and chief culprits for visual loss in most populations ( Mitchell et al., 2018 Wang and Lo, 2018 Ruiz-Medrano et al., 2019).Įarly diagnosis and prompt treatment were essential to achieve the best possible visual prognosis ( Mitchell et al., 2018 Wang and Lo, 2018 Ruiz-Medrano et al., 2019). It caused visual impairment and even blindness in both developed and developing countries ( Hong et al., 2013 Wolfram et al., 2019 Li et al., 2022b). With the rapid progress in population aging and the escalating prevalence of systemic diseases like hypertension and diabetes mellitus, as well as the increasing incidence of myopia in contemporary society, the morbidity rate of multiple ophthalmic diseases, especially with various retinal disorders, has ascended consequently. Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists ( p < 0.05).Ĭonclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening. 3School of Electronic and Information Engineering, Soochow University, Suzhou, China.2Suzhou Big Vision Medical Technology Co Ltd, Suzhou, China.1Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Tongji University School of Medicine, Shanghai, China. Jianhao Bai 1 † Zhongqi Wan 1 † Ping Li 1 Lei Chen 1 Jingcheng Wang 2 Yu Fan 2 Xinjian Chen 3* Qing Peng 1* Peng Gao 1*
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