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Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.
Ying, Bole; Chandra, Rajat S; Wang, Jianyong; Cui, Hongguang; Oatts, Julius T.
Afiliación
  • Ying B; Lower Merion High School, Ardmore, PA, USA.
  • Chandra RS; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Wang J; Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
  • Cui H; Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, P. R. China.
  • Oatts JT; Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA.
Transl Vis Sci Technol ; 13(8): 16, 2024 Aug 01.
Article en En | MEDLINE | ID: mdl-39120886
ABSTRACT

Purpose:

To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

Methods:

Cross-sectional study of children aged five to 18 years who underwent biometry and autorefraction before and after cycloplegia. Myopia was defined as cycloplegic spherical equivalent refraction (SER) ≤-0.5 Diopter (D). Models were evaluated for predicting SER using R2 and mean absolute error (MAE) and myopia status using area under the receiver operating characteristic (ROC) curve (AUC). Best-performing models were further evaluated using sensitivity/specificity and comparison of observed versus predicted myopia prevalence rate overall and in each age group. Independent data sets were used for training (n = 1938) and validation (n = 1476).

Results:

In the validation dataset, ML models predicted cycloplegic SER with high R2 (0.913-0.935) and low MAE (0.393-0.480 D). The AUC for predicting myopia was high (0.984-0.987). The best-performing model for SER (XGBoost) had high sensitivity and specificity (91.1% and 97.2%). Random forest (RF), the best-performing model for myopia, had high sensitivity and specificity (92.2% and 96.9%). Within each age group, difference between predicted and actual myopia prevalence was within 4%.

Conclusions:

Using noncycloplegic refractive error and ocular biometric data, ML models performed well for predicting cycloplegic SER and myopia status. When measuring cycloplegic SER is not feasible, ML may provide a useful tool for estimating cycloplegic SER and myopia prevalence rate in epidemiological studies. Translational Relevance Using ML to predict cycloplegic refraction based on noncycloplegic data is a powerful tool for large, population-based studies of refractive error.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Refracción Ocular / Aprendizaje Automático / Midriáticos / Miopía Límite: Adolescent / Child / Child, preschool / Female / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Transl Vis Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Refracción Ocular / Aprendizaje Automático / Midriáticos / Miopía Límite: Adolescent / Child / Child, preschool / Female / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Transl Vis Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos