Abstract
Myopia is a growing global health concern, and early detection and intervention are crucial for preventing its onset and progression. This study aims to predict myopia onset one year in advance by integrating biometric measurements with multimodal imaging data, including 3D optical coherence tomography (OCT) and color fundus photography (CFP). A dataset of 472 eyes from 347 subjects aged 6-14 years was collected, encompassing demographic information, biometric data, OCT, and CFP. Deep learning models were trained on OCT and CFP images to extract relevant features. A semi-supervised approach was employed to segment the choroid layer in OCT images, and the segmented images were then used to generate Early Treatment Diabetic Retinopathy Study (ETDRS) grid thickness value. An XGBoost model was developed to integrate image scores, ETDRS grid values, and biometric data for predicting myopia onset. The model incorporating all available data achieved an area under the receiver operating characteristic curve (AUROC) of 0.845 ± 0.050. Permutation importance analysis revealed that spherical equivalent was the most influential variable, followed by CFP scores, OCT scores, and ETDRS variables. The mixed model with multimodal information effectively captured the complex interactions and combined effects of the variables. These findings demonstrate the potential of integrating multimodal data to enhance the accuracy of myopia onset prediction, paving the way for personalized myopia management strategies.