A machine learning approach to predicting dry eye-related signs, symptoms and diagnoses from meibography images.

ABSTRACT:

Purpose

To use artificial intelligence to identify relationships between morphological characteristics of the Meibomian glands (MGs), subject factors, clinical outcomes, and subjective symptoms of dry eye.

Methods

A total of 562 infrared meibography images were collected from 363 subjects (170 contact lens wearers, 193 non-wearers). Subjects were 67.2 % female and were 54.8 % Caucasian. Subjects were 18 years of age or older. A deep learning model was trained to take meibography as input, segment the individual MG in the images, and learn their detailed morphological features. Morphological characteristics were then combined with clinical and symptom data in prediction models of MG function, tear film stability, ocular surface health, and subjective discomfort and dryness. T

SUBMITTER: Graham AD 

PROVIDER: S-EPMC11403426 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature