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

Purpose:The purpose of this study was to identify early indicators of keratoconus progression in Pentacam data using machine learning (ML) techniques.Methods:A retrospective Pentacam tabular data set was created by retrieving 11,760 tomography tests performed in patients with keratoconus. Data for eyes labeled unstable based on their referral for cross-linking were differentiated from data for eyes labeled stable and not referred for follow-up procedures. A boosted decision tree was trained on the final data set using a cross-validation method.Results:The final labeled data set included 1218 tomography tests. Training a ML model on a single test for each eye did not accurately predict disease progression, as indicated by the mean receiver-operating characteristic area under the curve of 0.59 +/- 0.1, with precision of 0.27, recall of 0.3, and F1 score of 0.28. Training on serial tests for each eye included 819 tomography scans and yielded good prognostic abilities: a receiver-operating characteristic area under the curve of 0.75 +/- 0.07, precision of 0.32, recall of 0.67, and F1 score of 0.43. In addition, 4 of the 55 Pentacam raw data parameters predominantly used the algorithm decision: age, central keratoconus index, Rs B, and D10 mm pachy.Conclusions: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.