Deep-Learning to Assess Biological Aging From Spinal Dual-energy X-ray Absorptiometry

Abstract

Spinal dual-energy absorptiometry (DXA) is an x-ray imaging method typically used to assess bone mineral density (BMD) and diagnose osteoporosis in post-menopausal women. However, these images may contain additional information beyond BMD to predict mortality and disease risk to help estimate biological age, the concept that chronological age can differ from the actual effects of aging. In this study, we aim to use deep learning to estimate biological age from spinal DXA images. A convolutional neural network was trained and tested on a 44,082 patient dataset from the UK Biobank database using a 5-fold cross-validation procedure and a hold-out test set of 8,951 individuals. An additional external data set from the Mass General Brigham (MGB), which consisted of 2,059 images, was used to evaluate the model on DXA obtained during routine care. We saw that the deep learning model output had a high association with several age-related incident and prevalent diseases, captured signals of aging and long-term risk for those diseases, and could categorize individuals at higher risk. In the MGB dataset, these associations were not able to be replicated, indicating that fine-tuning the model on DXAs obtained during routine clinical care may be necessary. As a result, our deep learning approach analyzing spinal DXA shows promise to be an effective way to measure biological age and as a method for doctors to help identify and prevent age-related diseases.