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Vision-based Assessment of Joint Extensibility in Ehlers Danlos Syndrome

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ClinicalTrials.gov Identifier: NCT05366114
Recruitment Status : Enrolling by invitation
First Posted : May 9, 2022
Last Update Posted : November 3, 2022
Information provided by (Responsible Party):
Nimish Mittal, University Health Network, Toronto

Brief Summary:

Ehlers Danlos Syndrome (EDS) is a heterogenous group of genetic disorders with 13 identified subtypes. Hypermobile EDS (hEDS), although the most common subtype of EDS, does not yet have an identified genetic mutation for diagnostic confirmation. Generalized joint hypermobility (GJH) is one of the hallmark features of hEDS. The scoring system used in measurement of GJH was described by Beighton. The Beighton score is calculated using a dichotomous scoring system to assess the extensibility of nine joints. Each joint is scored as either hypermobile (score = 1) or not hypermobile (score = 0). The total score (Beighton score) can vary between a minimum of 0 and a maximum of 9, with higher scores indicating greater joint laxity.

While there is moderate validity and inter-rater variability in using the Beighton score, there continue to be several challenges with its widespread and consistent application by clinicians. Some of the barriers reported in the literature include:

i) In open, non-standardized systems there can be significant variation in the method to perform these joint extensibility tests including assessing baseline measurements, ii) Determining consistent and standard measurement tools/methodology e.g. goniometer use can vary widely iii) Assessing the reliability of the cut off values and, iv) Performing full assessment prior to informing patients of possible classification of GJH positivity (low specificity and low positive predictive).

Inappropriate implementation of tests to assess GJH results in inaccurate identification of GJH and potentially unintended negative consequences of making the wrong diagnosis of EDS. The objective of this study is to create a more robust and valid method of joint mobility measurement and reduce error in the screening of EDS through use of a smartphone-based machine learning application systems for measurement of joint extensibility.

The project will:

i) Create a smart-phone enabled visual imaging app to assess the measurement of joint extensibility, ii) Assess the feasibility of using the smart-phone app in a clinical setting to screen potential EDS patients, iii) Determine the validity of the application in comparison to in person clinical assessment in a tertiary care academic EDS program. If successful, the smart-phone application could help standardize the care of potential EDS patients in an efficient and cost-effective manner.

Condition or disease Intervention/treatment
Ehlers-Danlos Syndrome Other: No intervention, additional video data collection only

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Study Type : Observational
Estimated Enrollment : 225 participants
Observational Model: Case-Only
Time Perspective: Cross-Sectional
Official Title: Assessing the Feasibility of a Smartphone-based, Machine Learning Visual Imaging Application for Assessment of Hyperextensibility of Peripheral Joints in Ehlers Danlos Syndrome
Actual Study Start Date : April 26, 2022
Estimated Primary Completion Date : May 2023
Estimated Study Completion Date : May 2023

Resource links provided by the National Library of Medicine

Group/Cohort Intervention/treatment
New patients at the GoodHope EDS clinic at Toronto General Hospital
All patients seen in the EDS clinic are eligible for inclusion, regardless of their presenting diagnosis or the results of their assessments.
Other: No intervention, additional video data collection only
No intervention will be used. Consenting participants will have video recordings taken during their exam of joint hypermobility which will be analyzed at a later time

Primary Outcome Measures :
  1. Comparison of agreement in predicted angle by pose-estimation library [ Time Frame: 4 months ]
    The performance of the developed machine learning models for predicting the range of motion will be analyzed by the pose-estimation library used. This analysis will be performed on the subset of the data collected during the first 2 months of data collection. This information will be used to select the pose-estimation libraries to proceed with when refining the machine learning models.

  2. Comparison of agreement in predicted angle by joint [ Time Frame: 1 year ]
    The performance of the developed machine learning models for predicting the range of motion at each joint (spine, knee, ankle, elbow, shoulder, thumb, fifth finger) will be analyzed independently for each joint. This will provide insight with respect to which joints the system is more accurate at predicting from video.

  3. Assess the accuracy of range of motion prediction using vision-based data [ Time Frame: 1 year ]
    Machine learning models trained on videos of individuals performing the joint hypermobility maneuvers will be developed. Their performance will be compared to the range of motion measured by an expert clinician using a goniometer.

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
The population being studied includes all patients referred to or seen in the GoodHope EDS clinic. The clinic accepts referrals from symptomatic adult patients (age > 18 years), with EDS, or suspected EDS. EDS is a connective tissue disorder with 100% penetrance, but variable in phenotypic expression, suspected cases of EDS or G-HSD may therefore include other hereditary or acquired connective tissue diseases/disorder, and/or complex chronic illnesses characterized by, or that feature, joint hypermobility, pain, and fatigue.

Inclusion Criteria:

  • All patients seen in the GoodHope EDS clinic at Toronto General are eligible for inclusion, regardless of their presenting diagnosis or the results of their assessments

Exclusion Criteria:

  • Patients who do not consent to participate will not be included (participants may withdraw consent at any time)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05366114

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Canada, Ontario
GoodHope EDS - Toronto General Hospital
Toronto, Ontario, Canada, M5G 2C4
Sponsors and Collaborators
University Health Network, Toronto
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Principal Investigator: Nimish Mittal, MD GoodHope Ehlers Danlos Syndrome Clinic, Toronto General Hospital
Critical Care Services Ontario, Ehlers-Danlos Syndrome Expert Panel Report, 2016. https://www.health.gov.on.ca/en/common/ministry/publications/reports/eds/Default.aspx.
He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 2961-9.
Fang H-S, Xie S, Tai Y-W, Lu C. RMPE: Regional Multi-person Pose Estimation. 2016 Nov 30; Available from: http://arxiv.org/abs/1612.00137
Lin T-Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, et al. Microsoft COCO: Common Objects in Context. 2014 May 1; Available from: http://arxiv.org/abs/1405.0312
Andriluka M, Pishchulin L, Gehler P, Schiele B. 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014. p. 3686-93
Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, et al. MediaPipe: A Framework for Building Perception Pipelines. 2019 Jun 14; Available from: https://arxiv.org/abs/1906.08172
Zhang F, Bazarevsky V, Vakunov A, Tkachenka A, Sung G, Chang C-L, et al. MediaPipe Hands: On-device Real-time Hand Tracking. 2020 Jun 17; Available from: http://arxiv.org/abs/2006.10214
Slembrouck M, Luong H, Gerlo J, Schütte K, Van Cauwelaert D, De Clercq D, et al. Multiview 3d Markerless Human Pose Estimation from Openpose Skeletons. In: International Conference on Advanced Concepts for Intelligent Vision Systems. Springer; 2020. p. 166-78.
Yahya M, Shah JA, Warsi A, Kadir K, Khan S, Izani M. Real time elbow angle estimation using single RGB camera. 2018 Aug 21; Available from: https://arxiv.org/abs/1808.07017
Shi B, Brentari D, Shakhnarovich G, Livescu K. Fingerspelling Detection in American Sign Language. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. p. 4166-75
Kim I-H, Jung I-H. A Study on Korea Sign Language Motion Recognition Using OpenPose Based on Deep Learning. 디지털콘텐츠학회논문지 (Journal of Digital Contents Society). 2021;22(4):681-7.

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Responsible Party: Nimish Mittal, Medical Director - GoodHope Ehlers Danlos Syndrome Clinic, University Health Network, Toronto
ClinicalTrials.gov Identifier: NCT05366114    
Other Study ID Numbers: 22-5073
First Posted: May 9, 2022    Key Record Dates
Last Update Posted: November 3, 2022
Last Verified: October 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: No IPD will be shared with other researchers.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Nimish Mittal, University Health Network, Toronto:
Ehlers-Danlos Syndrome
Beighton Score
Joint Range of Motion
Additional relevant MeSH terms:
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Ehlers-Danlos Syndrome
Pathologic Processes
Hemostatic Disorders
Vascular Diseases
Cardiovascular Diseases
Hemorrhagic Disorders
Hematologic Diseases
Skin Abnormalities
Congenital Abnormalities
Skin Diseases, Genetic
Genetic Diseases, Inborn
Collagen Diseases
Connective Tissue Diseases
Skin Diseases