AI-Powered Lateral DEXA Morphometry for Integrated Evaluation of Thoracic Kyphosis and Bone Density Assessment in Patients with Axial Spondyloarthritis

Axial spondyloarthritis (axSpA) is a chronic inflammatory disorder causing structural spinal damage and pathological thoracic kyphosis. Accurate quantification of spinal curvature is crucial for monitoring disease progression and guiding treatment. Conventional Cobb angle measurement on radiographs or DEXA images is widely used but is time-consuming and prone to inter-observer variability. This study evaluates an automated deep learning-based approach using a You Only Look Once (YOLO) model for vertebral detection on lateral morphometric DEXA scans and estimation of thoracic kyphosis angles. A dataset of 512 annotated DEXA images, including 182 from axSpA patients, was used to train and test the model. Kyphosis angles were computed by fitting a circle through detected vertebral centroids (Th4–Th12) and calculating the corresponding curvature angle. Model-predicted angles demonstrated strong agreement with physician-measured Cobb angles (r = 0.92, p < 0.001), low mean squared error (4.2°) and high sensitivity and specificity for detecting clinically significant kyphosis. Automated lateral DEXA morphometry provides a rapid, reproducible and clinically interpretable method for assessing thoracic kyphosis and bone density in axSpA, representing a practical tool for integrated structural and metabolic evaluation.