Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study.

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

SUBMITTER: Li Z 

PROVIDER: S-EPMC11227533 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature