![]() ![]() Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. ![]() Liu H, Li L, Wormstone IM, Qiao C, Zhang C, Liu P, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Li Z, He Y, Keel S, Meng W, Chang RT, He M. The impact of artificial intelligence in the diagnosis and management of glaucoma. Potential therapeutic usage of nanomedicine for glaucoma treatment. Prevalence of glaucoma in a rural northern china adult population: a population-based survey in kailu county, inner mongolia. Song W, Shan L, Cheng F, Fan P, Zhang L, Qu W, et al. Prevalence of primary open angle glaucoma in a rural adult Chinese population: the Handan eye study. Liang YB, Friedman DS, Zhou Q, Yang X, Sun LP, Guo LX, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Li JO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Applications of Artificial Intelligence in the Screening of Glaucoma in China. Jonas JB, Aung T, Bourne RR, Bron AM, Ritch R, Panda-Jonas S. With acceptable generalization capability across varying levels of image quality, different clinical centres, or certain retinal comorbidities, such as HM, the automatic AI diagnostic system had the potential to provide expert-level glaucoma detection. In validation dataset 3, the algorithm achieved comparable accuracy of 81.98% and AUC of 87.49%, with a sensitivity of 83.61% and specificity of 81.76% on GON recognition specifically in the HM population. On the subsets complicated with retinal comorbidities, such as diabetic retinopathy or age-related macular degeneration, in validation datasets 1 and 2, the algorithm achieved accuracy of 87.54% and 93.81%, and AUC of 97.02% and 97.46%, respectively. ![]() In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves (AUC) of 95.17% and 96.64%, and significantly higher sensitivity of 91.75% and 91.41%, respectively, compared to manual graders. The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated. We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort. ![]()
0 Comments
Leave a Reply. |