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.

Inclusion Criteria:

Slit-lamp images with sufficient diagnostic certainty and showing keratitis at the active phase.

Exclusion Criteria:

• Poor-quality images
• Images presenting mixed infections (i.e., cornea infected by two or more causative pathogens)

Child, Adult, Older Adult