Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning.

ABSTRACT:

Objective

To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos.

Design

Retrospective cohort study.

Participants

We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC.

Methods

We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets.

Mai

SUBMITTER: Abdelmotaal H 

PROVIDER: S-EPMC10587634 | biostudies-literature | 2024 Mar-Apr

REPOSITORIES: biostudies-literature