1. Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis
- Author
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Yifei Lu, Jian Wu, Junhui Shen, Daizhan Zhou, Wang Wenzhe, Chenqi Luo, Lei Feng, and Ke Yao
- Subjects
Adult ,Male ,0301 basic medicine ,Treatment response ,Fundus (eye) ,General Biochemistry, Genetics and Molecular Biology ,Young Adult ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Artificial Intelligence ,Diagnosis evaluation ,medicine ,Humans ,General Pharmacology, Toxicology and Pharmaceutics ,Aged ,Retinal necrosis ,General Veterinary ,Receiver operating characteristic ,business.industry ,Retinal Necrosis Syndrome, Acute ,Retinal ,General Medicine ,Middle Aged ,medicine.disease ,030104 developmental biology ,chemistry ,030220 oncology & carcinogenesis ,Female ,Acute retinal necrosis ,Artificial intelligence ,business ,Algorithm ,Algorithms ,Research Article ,Aided diagnosis - 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 (R (2)=0.7444, P
- Published
- 2021
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