Abstract
Purpose: This study aimed to investigate the features of progressive keratoconus by means of machine learning. Methods: In total, 163 eyes from 127 patients with at least 3 examination records were enrolled in this study. Pentacam HR was used to measure corneal topography. Steepest meridian keratometry (K(1)), flattest meridian keratometry (K(2)), steepest anterior keratometry (K(max)), central corneal thickness (CCT), thinnest corneal thickness (TCT), anterior radius of cornea (ARC), posterior elevation (PE), index of surface variation (ISV), and index of height deviation (IHD) were input for analysis. Support vector machine (SVM) and logistic regression analysis were applied to construct prediction models. Results: Age, PE, and IHD showed statistically significant differences as the follow-up period extended. K(2), PE, and ARC were selected for model construction. Logistic regression analysis presented a mean area under the curve (AUC) score of 0.780, while SVM presented a mean AUC of 0.659. The prediction sensitivity of SVM was 52.9%, and specificity was 79.0%. Conclusion: It is feasible to use machine learning to predict the progression and prognosis of keratoconus. Posterior elevation exhibits a sensitive prediction effect.