A web-based semi-supervised deep learning platform for automated AS-OCT assessment and monitoring of infectious keratitis

Infectious keratitis (IK) is a leading cause of corneal blindness globally. Accurate severity assessment is critical but remains limited by the subjective and limited nature of slit-lamp and anterior segment optical coherence tomography (AS-OCT) assessments. This study presents the 'Intelligent Quantitative Keratitis Image Analysis Platform', a web-based deep learning system for automated IK assessment through lesion segmentation, parameterization, stratification, and three-dimensional (3D) visualization based on AS-OCT data. A semi-supervised framework combining U-Net and Swin Transformer architectures was trained on 3130 AS-OCT images to address imaging artifacts, low contrast, scale variation, and irregular lesion boundaries. The model achieved Dice scores of 0.922 for corneal tissue and 0.834 for infiltrative lesions. Lesion characteristics were quantified within a polar coordinate system for radial stratification, yielding classification accuracies of 0.802 for infiltration thickness (IT) and 0.905 for infiltration width (IW). Intraclass correlation coefficients (ICCs) between AI and ground truth were 0.961 (IT) and 0.960 (IW), numerically exceeding the inter-expert agreement (ICCs: 0.786-0.955). Multimodel visualization via 3D corneal reconstructions aligned with slit-lamp images enabled anatomically coherent monitoring of disease progression. Overall, the platform provides a non-invasive, objective, and efficient approach for IK evaluation and monitoring.