Vision-based Assessment of Joint Extensibility in Ehlers Danlos Syndrome

• Conditions: Ehlers-Danlos Syndrome
• Interventions: Other: No intervention, additional video data collection only

• Registration Number: NCT05366114
• Lead Sponsor: 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-04-26
• Study First Submitted: 2022-05-04
• Study First Submitted QC: 2022-05-04
• Study First Posted: 2022-05-09
• Status Verified: 2023-11
• Last Update Submitted: 2023-11-27
• Last Update Posted: 2023-11-28
• Primary Completion: 2024-06
• Study Completion: 2024-12

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 225

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

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Exclusion Criteria

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

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
New patients at the GoodHope EDS clinic at Toronto General Hospital	No intervention, additional video data collection only	All patients seen in the EDS clinic are eligible for inclusion, regardless of their presenting diagnosis or the results of their assessments.

Primary Outcome Measures

Name	Time	Method
Comparison of agreement in predicted angle by joint	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.
Comparison of agreement in predicted angle by pose-estimation library	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.
Assess the accuracy of range of motion prediction using vision-based data	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.

Trial Locations

Locations (1)

GoodHope EDS - Toronto General Hospital; 🇨🇦 Toronto, Ontario, Canada