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

Abstract

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 discrimin..