Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. In this project, the investigators aim to develop an automated and accurate deep learning system to discriminate among bacterial, fungal, viral, amebic and noninfectious keratitis based on slit-lamp images and evaluated this system using the datasets obtained from mutiple independent clinical centers across China.
Study Type
OBSERVATIONAL
Enrollment
10,369
Ningbo Eye Hospital
Ningbo, Zhejiang, China
Eye Hospital of Wenzhou Medical University
Wenzhou, China
Area under the receiver operating characteristic curve of the deep learning system
Time frame: 2020-2022
Accuracy of the deep learning system
Time frame: 2020-2022
Sensitivity of the deep learning system
Time frame: 2020-2022
Specificity of the deep learning system
Time frame: 2020-2022
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