Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.

ABSTRACT:

Purpose

To develop and evaluate a novel artificial intelligence (AI)-derived metric for detection of early ectasia (E) by leveraging spatiotemporal biomechanical data derived from dynamic cornea videos only.

Design

Multicenter cross-sectional case-control retrospective study.

Participants

A total of 451 eyes from 451 patients from 2 centers with both Scheimpflug tomography and dynamic corneal deformation examinations.

Methods

The training data set included 167 normal (N) patients with stable eyes postlaser vision correction, 83 NT eyes with VAE (VAE-NT), and better eyes from 64 patients with bilateral keratoconus (ie, mild KC [MKC]). The external validation data set included 68, 33, and 36 patients from the N, VAE-NT, and MKC groups, respectively. Extensive spa

SUBMITTER: Abdelmotaal H 

PROVIDER: S-EPMC12648595 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature