The development of a machine learning model to train junior ophthalmologists in diagnosing the pre-clinical keratoconus.

ABSTRACT:

Purpose

This study aims to evaluate the diagnostic performance of a machine learning model (ML model) to train junior ophthalmologists in detecting preclinical keratoconus (PKC).

Methods

A total of 1,334 corneal topography images (The Pentacam HR system) from 413 keratoconus eyes, 32 PKC eyes and 222 normal eyes were collected. Five junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the ML model. The diagnostic performance of PKC was evaluated among three groups: junior ophthalmologist group (control group), ML model group and ML model-training junior ophthalmologist group (test group).

Results

The accuracy of the ML model between the eyes of patients with KC and NEs in all three clinics (99% accuracy, area under

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC11445185 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature