Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation.

Background: Generalized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model.

Objective: The proposed model is designed to provide the most accurate joint angle measurements in EDS appraisals.