Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis.

Lei Feng; Daizhan Zhou; Chenqi Luo; Junhui Shen; Wenzhe Wang; Yifei LU; Jian WU; Ke Yao

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Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis.

Author: Lei FENG ¹ ; Daizhan ZHOU ¹ ; Chenqi LUO ¹ ; Junhui SHEN ¹ ; Wenzhe WANG ² ; Yifei LU ² ; Jian WU ² ; Ke YAO ¹
Author Information

1. Eye Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
Publication Type:Journal Article
Keywords: Acute retinal necrosis (ARN); Artificial intelligence (AI) algorithm; Clinical application
From: Journal of Zhejiang University. Science. B 2021;22(6):504-511
CountryChina
Language:English
Abstract: The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (