Machine Learning–Based Identification of Risk Factors of Keratoconus Progression Using Raw Corneal Tomography Data

This study revealed specific dominant parameters attributing to the classification of stability, which are not routinely assessed in determining progression in common practice. Using ML techniques, keratoconus deterioration was evaluated algorithmically with training on multiple tests, yet was not predicted by a single tomography test. Hence, our study highlights novel factors to the current consideration of cross-linking referral and may serve as a supportive tool for clinicians.