Osteoporosis retinal prediction (ORION), predicting systemic health features from retinal images

Abstract

Osteoporosis is a silent but widespread condition that often goes undetected until advanced stages. In this study, a multi-view, multimodal deep learning model was proposed to act as a non-invasive biomarker for osteoporosis risk prediction by combining retinal fundus images with patient clinical data. Pretrained convolutional neural networks (CNNs), such as ResNet50, were used as feature extractors to leverage visual and non-visual cues, while a multilayer perceptron (MLP) was used to process the patients biodata. The model was trained and evaluated on a three-class classification task, targeting varying osteoporosis risk levels: Normal Bone Density, Low Bone Density, and Osteoporosis. Despite a modest F1-score of 50%, the model performed above random chance, indicating latent predictive signals in retinal imagery and patient metadata. These findings suggest that with further optimization and larger datasets, retinal-based screening may offer a viable early biomarker for osteoporosis risk assessment