Integrating biometric and multimodal imaging data for early prediction of myopia onset.

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

SUBMITTER: Hu M 

PROVIDER: S-EPMC12381001 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature