BACKGROUND
Accurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a larger number of patients for amblyopia risk.
OBJECTIVE
For efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep-learning-based system to predict the range of refractive error in children (mean age: 4.32±1.87 years) using 305 eccentric photorefraction images captured with a smartphone.
METHODS
Photorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction.
RESULTS
The trained deep-learning models resulted in an overall accuracy of 81.6%, with the following accuracy for each refractive error class: 80.0% in ≤ -5.0 diopters (D), 77.8% in > -5.0 D and ≤ -3.0 D, 82.0% in > -3.0 D and ≤ -0.5 D, 83.3% in > -0.5 D and < +0.5 D, 82.8% in ≥ +0.5 D and < +3.0 D, 79.3% in ≥ +3.0 D and < +5.0 D, and 75.0% in ≥ +5.0 D. These results indicate that our deep-learning-based system performed sufficiently accurately.
CONCLUSIONS
This study demonstrated the potential for precise smartphone-based prediction systems for refractive error using deep learning and, further, yielded a robust collection of pediatric photorefraction images.
CLINICALTRIAL
Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation
