Myopia prediction for adolescents via time-aware deep learning

TL;DR

This study employs a time-aware deep learning model to predict adolescent myopia progression using longitudinal vision data, achieving high accuracy and early detection capabilities.

Contribution

The paper introduces a novel application of Time-Aware LSTM for predicting myopia progression from irregular time series data in adolescents.

Findings

Mean absolute error of 0.273 for spherical equivalent prediction.

Time-aware LSTM effectively captures temporal features in irregular data.

Model's error is below the clinically acceptable threshold.

Abstract

Background: Quantitative prediction of the adolescents' spherical equivalent based on their variable-length historical vision records. Methods: From October 2019 to March 2022, we examined binocular uncorrected visual acuity, axial length, corneal curvature, and axial of 75,172 eyes from 37,586 adolescents aged 6-20 years in Chengdu, China. 80\% samples consist of the training set and the remaining 20\% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the adolescents' spherical equivalent within two and a half years. Result: The mean absolute prediction error on the testing set was 0.273-0.257 for spherical equivalent, ranging from 0.189-0.160 to 0.596-0.473 if we consider different lengths of historical records and different prediction durations. Conclusions: Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.273 is much smaller than the criterion for clinically acceptable prediction, say 0.75.