Automated, Vision-Based Goniometry and Range of Motion Calculation in Individuals With Suspected Ehlers-Danlos Syndromes/Generalized Hypermobility Spectrum Disorders: A Comparison of Pose-Estimation Libraries to Goniometric Measurements

Generalized joint hypermobility (GJH) often leads clinicians to suspect a diagnosis of Ehlers Danlos Syndrome (EDS), but it can be difficult to objectively assess. Video-based goniometry has been proposed to objectively estimate joint range of motion in hyperextended joints. As part of an exam of joint hypermobility at a specialized EDS clinic, a mobile phone was used to record short videos of 97 adults (89 female, 35.0 ± 9.9 years old) undergoing assessment of the elbows, knees, shoulders, ankles, and fifth fingers. Five body keypoint pose-estimation libraries (AlphaPose, Detectron, MediaPipe-Body, MoveNet - Thunder, OpenPose) and two hand keypoint pose-estimation libraries (AlphaPose, MediaPipe-Hands) were used to geometrically calculate the maximum angle of hyperextension or hyperflexion of each joint. A custom domain-specific model with a MobileNet-v2 backbone finetuned on data collected as part of this study was also evaluated for the fifth finger movement. Spearman's correlation was used to analyze the angles calculated from the tracked joint positions, the angles calculated from manually annotated keypoints, and the angles measured using a goniometer. Moderate correlations between the angles estimated using pose-tracked keypoints and the goniometer measurements were identified for the elbow (rho =.722; Detectron), knee (rho =.608; MoveNet - Thunder), shoulder (rho =.632; MoveNet - Thunder), and fifth finger (rho =.786; custom model) movements. The angles estimated from keypoints predicted by open-source libraries at the ankles were not significantly correlated with the goniometer measurements. Manually annotated angles at the elbows, knees, shoulders, and fifth fingers were moderately to strongly correlated to goniometer measurements but were weakly correlated for the ankles. There was not one pose-estimation library which performed best across all joints, so the library of choice must be selected separately for each joint of interest. This work evaluates several pose-estimation models as part of a vision-based system for estimating joint angles in individuals with suspected joint hypermobility. Future applications of the proposed system could facilitate objective assessment and screening of individuals referred to specialized EDS clinics.

Feasibility of video-based joint hypermobility assessment in individuals with suspected Ehlers-Danlos syndromes/generalised hypermobility spectrum disorders: a single-site observational study protocol

INTRODUCTION

Ehlers-Danlos syndromes (EDS)/generalised hypermobility spectrum disorders (G-HSD) affect the connective tissue of the body and present with a heterogeneous set of symptoms that pose a challenge for diagnosis. One of the main diagnostic criteria of EDS/G-HSD is generalised joint hypermobility, which is currently assessed by clinicians during a physical exam. However, the practice for measuring joint hypermobility is inconsistent between clinicians, leading to high inter-rater variability. Often patients are misdiagnosed with EDS/G-HSD based on an incorrect hypermobility assessment, leading to increased referral rates and resource utilisation at specialised EDS clinics that results in unnecessary emotional distress for patients. An objective, validated and scalable method for assessing hypermobility might mitigate these issues and result in improved EDS/G-HSD patient care.

METHODS AND ANALYSIS

This study will examine the use of videos obtained using a smartphone camera to assess the range of motion (ROM) and hypermobility of the joints assessed in Beighton score and more (spine, shoulders, elbows, knees, ankles, thumbs and fifth fingers) in individuals with suspected EDS/G-HSD. Short videos of participants will be captured as they undergo a formal assessment of joint hypermobility at the GoodHope EDS Clinic at Toronto General Hospital. Clinicians will measure the ROM at each joint using a clinical-grade goniometer to establish ground truth measurements. Open-source human pose-estimation libraries will be used to extract the locations of key joints from the videos. Deterministic and machine learning systems will be developed and evaluated for estimating the ROM at each joint. Results will be analysed separately for each joint and human pose-estimation library.

ETHICS AND DISSEMINATION

This study was approved by the Research Ethics Board of the University Health Network in Toronto on 26 April 2022. Participants will provide written informed consent. Findings from this study will be published in peer-reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

NCT05366114.