A Novel Time-Aware Deep Learning Model Predicting Myopia in Children and Adolescents.

Objective: To quantitatively predict children's and adolescents' spherical equivalent (SE) by leveraging their variable-length historical vision records. Design: Retrospective analysis. Participants: Eight hundred ninety-five myopic children and adolescents aged 4 to 18 years, with a complete ophthalmic examination and retinoscopy in cycloplegia prior to spectacle correction, were enrolled in the period from January 1, 2008 to July 1, 2023 at the University Hospital "Sveti Duh," Zagreb, Croatia. Methods: A novel modification of time-aware long short-term memory (LSTM) was used to quantitatively predict children's and adolescents' SE within 7 years after diagnosis. Main Outcome Measures: The utilization of extended gate time-aware LSTM involved capturing temporal features within irregularly sampled time series data. This approach aligned more closely with the characteristics of fact-based data, increasing its applicability and contributing to the early identification of myopia progression. Results: The testing set exhibited a mean absolute prediction error (MAE) of 0.10 ± 0.15 diopter (D) for SE. Lower MAE values were associated with longer sequence lengths, shorter prediction durations, older age groups, and low myopia, while higher MAE values were observed with shorter sequence lengths, longer prediction durations, younger age groups, and in premyopic or high myopic individuals, ranging from as low as 0.03 ± 0.04 D to as high as 0.45 ± 0.24 D. Conclusions: Extended gate time-aware LSTM capturing temporal features in irregularly sampled time series data can be used to quantitatively predict children's and adolescents' SE within 7 years with an overall error of 0.10 ± 0.15 D. This value is substantially lower than the threshold for prediction to be considered clinically acceptable, such as a criterion of 0.75 D. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.