Predicting Early Keratoconus Progression Using Biomechanics via Multi-Machine Learning: A Multicenter 2-Year...

This study aimed to develop and validate a biomechanical early progression index (BEPI) based on machine learning algorithms, intended to predict early keratoconus progression at the first visit.

This multicenter prospective cohort study recruited 247 eyes of 247 participants from 3 centers; 225 completed all visits and were divided into a training and validation cohort. Corvis ST and Pentacam were used to obtain biomechanical and tomographic data. Based on disease progression during the follow-up period, predictive models were constructed using biomechanical data by applying Random Forest, XGBoost, Naïve Bayes classifier, K-Nearest Neighbours and Support Vector Machine.

In the training cohort (n = 157), 66 eyes remained stable after a mean period of 25.18 ± 4.17 months, whereas 91 eyes demonstrated progression after 6.74 ± 2.53 months. The random forest provided the highest predictive performance and was designated as BEPI. With a cutoff value of 0.528, BEPI achieved 0.989 sensitivity and 0.985 specificity in distinguishing between stable and progressive cases. In the external validation cohort (n = 68), 31 eyes remained stable after 23.16 ± 6.75 months and 37 eyes progressed after 7.03 ± 3.42 months of follow-up. BEPI demonstrated the highest predictive performance, with an accuracy of 0.941 and an F1-score of 0.947.

The machine learning-based index BEPI derived from corneal biomechanics enabled the accurate prediction of early keratoconus progression at the first visit, with an accuracy of 0.941. BEPI provides clinicians with a novel index for the accurate evaluation of early keratoconus progression, facilitating personalised clinical decision-making to preserve vision and improve prognosis.