ArticleJournal of Zhejiang University. Science. B2021
Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis.
Article in Journal of Zhejiang University. Science. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Automated early detection of acute retinal necrosis from ultra-widefield color fundus photography using deep learning.Eye and vision (London, England) · 2024Article
- Diagnostic and therapeutic challenges in acute retinal necrosis; an update.Eye (London, England) · 2024Review
- Clinical application of CT-based radiomics model in differentiation between laryngeal squamous cell carcinoma and squamous cell hyperplasia.Frontiers in medicine · 2023Article
- Research and application advances in rehabilitation assessment of stroke.Journal of Zhejiang University. Science. B · 2022Review
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 (
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