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On-line Access: 2021-06-11

Received: 2020-06-29

Revision Accepted: 2020-11-19

Crosschecked: 0000-00-00

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ke Yao

https://orcid.org/0000-0002-6764-7365

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Journal of Zhejiang University SCIENCE B 2021 Vol.22 No.6 P.504-511

http://doi.org/10.1631/jzus.B2000343


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


Author(s):  Lei FENG, Daizhan ZHOU, Chenqi LUO, Junhui SHEN, Wenzhe WANG, Yifei LU, Jian WU, Ke YAO

Affiliation(s):  Eye Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; more

Corresponding email(s):   xlren@zju.edu.cn

Key Words:  Acute retinal necrosis (ARN), Artificial intelligence (AI) algorithm, Clinical application


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.

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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"
}

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%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
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%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.

人工智能技术在急性视网膜坏死诊治中的应用

目的:探讨人工智能技术评估急性视网膜坏死病灶的有效性和准确性。
创新点:本研究首次将人工智能技术应用于急性视网膜坏死的诊治,有利于减少该疾病的误诊率,以及弥补治疗前后定量分析的空白。
方法:随机选取眼科就诊的急性视网膜坏死患者32人41眼,收集149张眼底广角眼底拍照,眼科专家对数据进行标注,选取合适的模型建模、数据集学习、训练,评估,评估学习训练后的人工智模型在识别视网膜坏死区域的准确性、敏感性和特异性。观察和比较临床治疗前后模型识别的视网膜坏死灶面积改变,并分析其与房水病毒载量的关系。
结论:人工智能在急性视网膜坏死的诊疗中具有良好的应用价值,值得进一步研究和推广。

关键词:急性视网膜坏死;人工智能;临床应用

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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