Prediction of Maximum Keratometry Progression With Stratified Progression Criteria in Keratoconus Patients Using Machine Learning.

Purpose: The aim of this study was to use machine learning to predict 12-months progression of maximum keratometry (Kmax) within stratified repeatability limits based on a baseline topography/optical coherence tomography measurement in patients with keratoconus (KC). Methods: This study included patients with diagnosed KC in which baseline and follow-up measurements were performed with a Placido disc topographer combined with optical coherence tomography (OCT) at a time interval of at least 7 months. Eyes were classified as "progressive" or "stable" based on the repeatability-adjusted difference in Kmax between the baseline measurement and a follow-up measurement. Nested cross-validation was used to train different algorithms, and the algorithm with the best area under the curve (AUC) value was used as final model. Results: A total of 102 eyes of 65 patients were included. Mean age was 33.2 ± 13.0 years. Predictors with high outer fold stability included specific KC markers such as the CSIB index and posterior K values. Within the internal folds, the model achieved an AUC of 0.82, a sensitivity of 62.2%, and a specificity of 83.2% and, in the outer left-out folds, a sensitivity of 65.6%, a specificity of 84.3%, and thus an accuracy of 78.4%. Conclusions: Progression of Kmax can be predicted with decent accuracy using a single OCT-based topographic measurement. Nested cross-validation represents a valuable tool in small datasets. Translational Relevance: Machine learning-based prediction of Kmax progression may aid in scheduling risk-adapted follow-up appointments, potentially leading to more rapid identification of progression-suspect KC cases.