AI-assisted automated interpretation of corneal topography in orthokeratology patients: enhancing diagnostic precision and efficiency.

AIM: To evaluate the potential of artificial intelligence (AI) for automating corneal topography interpretation in orthokeratology patients, aiming to enhance diagnostic precision, efficiency, and clinical decision-making in myopia management. METHODS: The 1469 corneal topography images from 582 eyes of 326 myopic children treated with orthokeratology lenses over 47mo were collected. Each sample was categorized by decentration, treatment zone size, shape variation, and eye laterality. A multi-task AI model was developed to predict these parameters, with performance measured using area under curve (AUC), accuracy, and F1 scores. We compared AI-only, human-only, and combined Human+AI approaches on a subset of 100 images. External validation with images from additional hospitals tested model generalizability. RESULTS: The model achieved high accuracy in eye-side prediction (AUC 0.95) and AUC values of 0.52-0.74 for decentration, treatment zone, and shape variation tasks. The combined Human+AI method outperformed AI-only and human-only approaches, achieving the highest accuracy (up to 87%) and fastest processing time (80ms). External validation confirmed robust performance in simple tasks, though accuracy was lower for complex classifications due to imaging variations. CONCLUSION: AI provides efficient routine corneal topography assessments, while complex cases benefit most from a Human+AI approach, particularly in scenarios requiring nuanced clinical interpretation. The model currently functions as an assistive tool.