Stratification of keratoconus progression using unsupervised machine learning analysis of tomographical parameters

Purpose: This study aimed to stratify eyes with keratoconus (KC) based on longitudinal changes in all Pentacam parameters into clusters using unsupervised machine learning, with the broader objective of more clearly defining the characteristics of KC progression. Methods: A data-driven cluster analysis (hierarchical clustering) was undertaken on a retrospective cohort of 1017 kC eyes and 128 control eyes. Clusters were derived using 6-month tomographical change in individual eyes from analysis of the reduced dimensionality parameter space using all available Pentacam parameters (406 principal components). The optimal number of clusters was determined by the clustering's capacity to discriminate progression between KC and control eyes based on change across parameters. One-way ANOVA was used to compare parameters between inferred clusters. Complete Pentacam data changes at 6, 12 and 18-month time points provided validation datasets to determine the generalizability of the clustering model. Results: We identified three clusters in KC progression patterns. Eyes designated within cluster 3 had the most rapidly changing tomographical parameters compared to eyes in either cluster 1 or 2. Eyes designated within cluster 1 reflected minimal changes in tomographical parameters, closest to the tomographical changes of control (non-KC) eyes. Thirty-nine corneal curvature parameters were identified and associated with these stratified clusters, with each of these parameters changing significantly different between three clusters. Similar clusters were identified at the 6, 12 and 18-month follow-up. Conclusions: The clustering model developed was able to automatically detect and categorize KC tomographical features into fast, slow, or limited change at different time points. This new KC stratification tool may provide an opportunity to provide a precision medicine approach to KC.