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A machine learning approach to predicting dry eye-related signs, symptoms and diagnoses from meibography images.
Graham, Andrew D; Kothapalli, Tejasvi; Wang, Jiayun; Ding, Jennifer; Tse, Vivien; Asbell, Penny A; Yu, Stella X; Lin, Meng C.
Afiliación
  • Graham AD; Vision Science Group, University of California, Berkeley, United States.
  • Kothapalli T; Clinical Research Center, School of Optometry, University of California, Berkeley, United States.
  • Wang J; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States.
  • Ding J; Clinical Research Center, School of Optometry, University of California, Berkeley, United States.
  • Tse V; Vision Science Group, University of California, Berkeley, United States.
  • Asbell PA; Clinical Research Center, School of Optometry, University of California, Berkeley, United States.
  • Yu SX; Clinical Research Center, School of Optometry, University of California, Berkeley, United States.
  • Lin MC; Vision Science Group, University of California, Berkeley, United States.
Heliyon ; 10(17): e36021, 2024 Sep 15.
Article en En | MEDLINE | ID: mdl-39286076
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. The models were analyzed to identify the most heavily weighted features used by the algorithm for predictions.

Results:

MG morphological characteristics were heavily weighted predictors for eyelid notching and vascularization, MG expressate quality and quantity, tear film stability, corneal staining, and comfort and dryness ratings, with accuracies ranging from 65 % to 99 %. Number of visible MG, along with other clinical parameters, were able to predict MG dysfunction, aqueous deficiency and blepharitis with accuracies ranging from 74 % to 85 %.

Conclusions:

Machine learning-derived MG morphological characteristics were found to be important in predicting multiple signs, symptoms, and diagnoses related to MG dysfunction and dry eye. This deep learning method illustrates the rich clinical information that detailed morphological analysis of the MGs can provide, and shows promise in advancing our understanding of the role of MG morphology in ocular surface health.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido