| Autor: |
Bing Li, Huan Chen, Weihong Yu, Ming Zhang, Fang Lu, Jingxue Ma, Yuhua Hao, Xiaorong Li, Bojie Hu, Lijun Shen, Jianbo Mao, Xixi He, Hao Wang, Dayong Ding, Xirong Li, Youxin Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
npj Digital Medicine, Vol 7, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2398-6352 |
| DOI: |
10.1038/s41746-023-00991-9 |
| Popis: |
Abstract Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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