Open AccessDeep Learning for Automatic Detection of Recurrent Retinal Detachment after Surgery Using Ultra‐Widefield Fundus Images: A Single‐Center Study
Author(s) -
Zhou Wen-Da,
Dong Li,
Zhang Kai,
Wang Qian,
Shao Lei,
Yang Qiong,
Liu Yue-Ming,
Fang Li-Jian,
Shi Xu-Han,
Zhang Chuan,
Zhang Rui-Heng,
Li He-Yan,
Wu Hao-Tian,
Wei Wen-Bin
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200067
Subject(s) - fundus (uterus) , medicine , ophthalmology , confidence interval , prospective cohort study , retinal detachment , receiver operating characteristic , retinal , single center , standard deviation , artificial intelligence , surgery , computer science , mathematics , statistics
It is important to detect recurrent retinal detachment (RD) among patients after retinal reattachment surgery. The application of deep learning (DL) methods to detect recurrent RD with ultra‐widefield (UWF) fundus images is promising, but the feasibility and efficiency have not been studied. A DL system with ResNet‐50 and Inception‐ResNet‐V2 is developed and internally validated to identify recurrent RD and retina reattachment after surgery. The performance is further validated and compared with human ophthalmologists in a prospective dataset assessed by area under curve (AUC), accuracy, sensitivity, and specificity. Five hundred fifty‐four UWF fundus images from 173 RD patients (mean [standard deviation] age: 39.2 ± 16.2 years; male: 115 [66.5%]) are used to develop the DL system. DL shows AUCs of 0.912 (95% confidence interval [CI]: 0.855–0.968) and 0.906 (95% CI: 0.818–0.995) for the two models. Eighty‐nine UWF fundus images from 23 RD patients (mean [standard deviation] age: 31.4 ± 12.3 years; male: 15 [65.2%]) are collected as prospective dataset. DL also shows the ability to detect recurrent RD with the AUCs of 0.929 and 0.930 for the two models, respectively. DL reaches a similar and even better diagnostic performance than junior ophthalmologists and performs much better than medical students.