Data-Driven Detection of Subclinical Keratoconus via Semi-Supervised Clustering of Multidimensional Corneal Biomarkers.

ABSTRACT:

Purpose

To objectively identify subclinical keratoconus (SKC) from a large sample of healthy and keratoconus (KC) patients via a data-driven framework on corneal imaging data from an anterior-segment OCT (AS-OCT) device (MS-39, CSO Italia).

Design

A retrospective cohort study.

Subjects

At 2 sites within the Moorfields Eye Hospital network in London, United Kingdom, 25 816 corneal scans from 5005 patients, including 3605 with KC and 1400 healthy control patients, were acquired between 2020 and 2024.

Methods

Principal component analysis (PCA) followed by Gaussian mixture modeling (GMM) was applied to AS-OCT-derived data, including 20 KC indices and patient age, to identify SKC eyes, which were then statistically compared against healthy and KC eyes. Subclinical K

SUBMITTER: Kandakji L 

PROVIDER: S-EPMC12756640 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature