1. Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study
- Author
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Zhongwen Li, He Xie, Zhouqian Wang, Daoyuan Li, Kuan Chen, Xihang Zong, Wei Qiang, Feng Wen, Zhihong Deng, Limin Chen, Huiping Li, He Dong, Pengcheng Wu, Tao Sun, Yan Cheng, Yanning Yang, Jinsong Xue, Qinxiang Zheng, Jiewei Jiang, and Wei Chen
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
- Published
- 2024
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