Evidence map›Paper›PMID 41639883›Full record

ArticleBiomedical engineering online2026

Development and evaluation of a vision pose-tracking based Beighton score tool for generalized joint hypermobility in individuals with suspected Ehlers-Danlos syndromes.

Andrea Sabo, Babak Taati, Amol Deshpande, Nimish Mittal

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Article in Biomedical engineering online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Andrea SaboKITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 550 University Avenue, Toronto, ON, M5G A2A, Canada.
Babak TaatiKITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 550 University Avenue, Toronto, ON, M5G A2A, Canada.
Amol DeshpandeFaculty of Medicine, University of Toronto, Toronto, ON, Canada.
Nimish MittalTemerty Faculty of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, ON, Canada. Nimish.Mittal@uhn.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneralized joint hypermobility (GJH) is often challenging to assess, but its presence could suggest a syndromic diagnosis of Ehlers-Danlos Syndromes (EDS).

objectiveAn automated and objective method for estimating joint hypermobility with Beighton score using short video clips is proposed.

methodA total of 225 adults (91.8% female, median age 32.0, range 18-64) referred to a specialized EDS clinic were recruited for this study. A video-based method relying on pose-estimation libraries was developed to predict per-joint hypermobility of both elbows, knees, fifth fingers, thumbs, and spine; as well as the overall Beighton score. The system was developed on the first 100 individuals (training set), and validated on the remaining 125 individuals (test set).

resultsThe system screened out 31.9% of the training set and 32.0% of the test set as not having GJH, while recalling 89.1% and 91.9% of the true positives on the train and test set, respectively. The consistency of the system between the training and test sets suggests that it generalizes well to unseen individuals. The system was tuned to be with a focus on sensitivity to avoid screening out individuals with GJH. As such, the specificity of the system is 52.1% on the training set and 42.4% on the test set.

conclusionThe proposed system can objectively screen individuals for possible GJH and also screen out those without GJH during the referral process, reducing the burden on specialized EDS clinics while providing early diagnostic triage. Future research will focus on deploying the tool as a mobile application.

Indexed as

Ehlers-Danlos SyndromeImage Processing, Computer-AssistedJoint InstabilityVision, OcularAdolescentAdultFemaleHumansMaleMiddle AgedVideo RecordingYoung AdultEhlers-Danlos syndromesGeneralized joint hypermobilityHuman pose-estimationRange of motion estimationVideo-based goniometry

Identifiers

PMID41639883
PMCPMC12964692

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.