CLC number:
On-line Access: 2021-06-11
Received: 2020-06-29
Revision Accepted: 2020-11-19
Crosschecked: 0000-00-00
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Lei FENG, Daizhan ZHOU, Chenqi LUO, Junhui SHEN, Wenzhe WANG, Yifei LU, Jian WU, Ke YAO. Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis[J]. Journal of Zhejiang University Science B, 2021, 22(6): 504-511.
@article{title="Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis",
author="Lei FENG, Daizhan ZHOU, Chenqi LUO, Junhui SHEN, Wenzhe WANG, Yifei LU, Jian WU, Ke YAO",
journal="Journal of Zhejiang University Science B",
volume="22",
number="6",
pages="504-511",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2000343"
}
%0 Journal Article
%T Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis
%A Lei FENG
%A Daizhan ZHOU
%A Chenqi LUO
%A Junhui SHEN
%A Wenzhe WANG
%A Yifei LU
%A Jian WU
%A Ke YAO
%J Journal of Zhejiang University SCIENCE B
%V 22
%N 6
%P 504-511
%@ 1673-1581
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2000343
TY - JOUR
T1 - Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis
A1 - Lei FENG
A1 - Daizhan ZHOU
A1 - Chenqi LUO
A1 - Junhui SHEN
A1 - Wenzhe WANG
A1 - Yifei LU
A1 - Jian WU
A1 - Ke YAO
J0 - Journal of Zhejiang University Science B
VL - 22
IS - 6
SP - 504
EP - 511
%@ 1673-1581
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2000343
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 (R2=0.7444, P<0.0001) and therapeutic response of ARN (R2=0.999, P<0.0001). Therefore, our AI algorithm has a potential application in the clinical aided diagnosis of ARN, evaluation of ARN severity, and treatment response monitoring.
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