Ehlers-Danlos SyndromeNCT05366114
Vision-based Assessment of Joint Extensibility in Ehlers Danlos Syndrome
An observational study of Ehlers-Danlos Syndrome, run by University Health Network, Toronto at 1 site in Canada.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Ehlers-Danlos Syndrome
- How many people
- 225 participants
- Where
- 1 siteCanada
- Study dates
- 26 Apr 2022to Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Patients who do not consent to participate will not be included (participants may withdraw consent at any time)
Trial locations and contacts
1 site in 1 country.
About this study
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
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:
Read more
- 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:
- 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.
- Conditions
- Ehlers-Danlos Syndrome
- Being tested
- No intervention, additional video data collection only
- Keywords
- Ehlers-Danlos Syndrome, Computer-vision, Video, Smartphone, Beighton Score, Joint Range of Motion
- Medical subject headings
- Ehlers-Danlos Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention, additional video data collection onlyOther
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
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: No intervention, additional video data collection only |
What is being measured
The primary outcome is the main question the study is designed to answer.
Comparison of agreement in predicted angle by pose-estimation library
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.
Measured: 4 months
Comparison of agreement in predicted angle by joint
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.
Measured: 1 year
Assess the accuracy of range of motion prediction using vision-based data
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.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 May 2022
- Study start26 Apr 2022
- Primary completionJun 2024 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (21)Papers the sponsor has linked to this study.
- Other trials for Ehlers-Danlos SyndromeStudies of the same condition, open and under way.
- More trials run by University Health Network, TorontoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 225 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Health Network, Toronto
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nimish Mittal, University Health Network, Toronto
- Investigators
- Nimish Mittal, MD, Principal investigator, GoodHope Ehlers Danlos Syndrome Clinic, Toronto General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05366114
- Sponsor's protocol ID
- 22-5073
- Status last verified
- Nov 2023
- First submitted
- 4 May 2022
- Last updated
- 28 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No IPD will be shared with other researchers.
Publications
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.
Cahill SV, Sharkey MS, Carter CW. Clinical assessment of generalized ligamentous laxity using a single test: is thumb-to-forearm apposition enough? J Pediatr Orthop B. 2021 May 1;30(3):296-300. doi: 10.1097/BPB.0000000000000732.
PubMed 32301823He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 2961-9.
Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Trans Pattern Anal Mach Intell. 2021 Jan;43(1):172-186. doi: 10.1109/TPAMI.2019.2929257. Epub 2020 Dec 4.
PubMed 31331883Fang 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
Mehdizadeh S, Nabavi H, Sabo A, Arora T, Iaboni A, Taati B. Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions. J Neuroeng Rehabil. 2021 Sep 15;18(1):139. doi: 10.1186/s12984-021-00933-0.
PubMed 34526074Sabo A, Mehdizadeh S, Ng KD, Iaboni A, Taati B. Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data. J Neuroeng Rehabil. 2020 Jul 14;17(1):97. doi: 10.1186/s12984-020-00728-9.
PubMed 32664973Lu M, Zhao Q, Poston KL, Sullivan EV, Pfefferbaum A, Shahid M, Katz M, Montaser-Kouhsari L, Schulman K, Milstein A, Niebles JC, Henderson VW, Fei-Fei L, Pohl KM, Adeli E. Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos. Med Image Anal. 2021 Oct;73:102179. doi: 10.1016/j.media.2021.102179. Epub 2021 Jul 21.
PubMed 34340101Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, Alty JE. The discerning eye of computer vision: Can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. 2020 Sep 15;416:117003. doi: 10.1016/j.jns.2020.117003. Epub 2020 Jun 30.
PubMed 32645513Ota M, Tateuchi H, Hashiguchi T, Kato T, Ogino Y, Yamagata M, Ichihashi N. Verification of reliability and validity of motion analysis systems during bilateral squat using human pose tracking algorithm. Gait Posture. 2020 Jul;80:62-67. doi: 10.1016/j.gaitpost.2020.05.027. Epub 2020 May 25.
PubMed 32485426Slembrouck 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.
Study at a glance
From the registry record, last updated by the sponsor on 28 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT05366114
- Acronym / sponsor ID
- 22-5073
- Conditions
- Ehlers-Danlos Syndrome
- Keywords
- Ehlers-Danlos Syndrome, Computer-vision, Video, Smartphone, Beighton Score, Joint Range of Motion
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 225 participants (estimated)
- Lead sponsor
- University Health Network, TorontoUniversity, hospital or other
- Study start
- 26 Apr 2022 (actual)
- Primary completion
- Jun 2024 (estimated)
- Study completion
- Dec 2024 (estimated)
- First posted
- 9 May 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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:
- 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.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| New patients at the GoodHope EDS clinic at Toronto General Hospital | – | Other: No intervention, additional video data collection onlyAll patients seen in the EDS clinic are eligible for inclusion, regardless of their presenting diagnosis or the results of their assessments. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- 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
Key exclusion criteria (1 listed)
- Patients who do not consent to participate will not be included (participants may withdraw consent at any time)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention, additional video data collection onlyNo 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 | Other | New patients at the GoodHope EDS clinic at Toronto General Hospital |
Primary outcome
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.
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.
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Nov 2023.
- BackgroundCritical Care Services Ontario, Ehlers-Danlos Syndrome Expert Panel Report, 2016. https://www.health.gov.on.ca/en/common/ministry/publications/reports/eds/Default.aspx.
- Background: PubMed 32301823Cahill SV, Sharkey MS, Carter CW. Clinical assessment of generalized ligamentous laxity using a single test: is thumb-to-forearm apposition enough? J Pediatr Orthop B. 2021 May 1;30(3):296-300. doi: 10.1097/BPB.0000000000000732.
- BackgroundHe K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 2961-9.
- Background: PubMed 31331883Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Trans Pattern Anal Mach Intell. 2021 Jan;43(1):172-186. doi: 10.1109/TPAMI.2019.2929257. Epub 2020 Dec 4.
- BackgroundFang 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
- BackgroundLin 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
- BackgroundAndriluka 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
- BackgroundLugaresi 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
- BackgroundZhang 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
- Background: PubMed 34526074Mehdizadeh S, Nabavi H, Sabo A, Arora T, Iaboni A, Taati B. Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions. J Neuroeng Rehabil. 2021 Sep 15;18(1):139. doi: 10.1186/s12984-021-00933-0.
- Background: PubMed 32664973Sabo A, Mehdizadeh S, Ng KD, Iaboni A, Taati B. Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data. J Neuroeng Rehabil. 2020 Jul 14;17(1):97. doi: 10.1186/s12984-020-00728-9.
- Background: PubMed 34340101Lu M, Zhao Q, Poston KL, Sullivan EV, Pfefferbaum A, Shahid M, Katz M, Montaser-Kouhsari L, Schulman K, Milstein A, Niebles JC, Henderson VW, Fei-Fei L, Pohl KM, Adeli E. Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos. Med Image Anal. 2021 Oct;73:102179. doi: 10.1016/j.media.2021.102179. Epub 2021 Jul 21.
- Individual participant dataSharing planned: No. No IPD will be shared with other researchers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Nimish Mittal, MD principal investigator
GoodHope Ehlers Danlos Syndrome Clinic, Toronto General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ehlers-Danlos Syndrome, run by University Health Network, Toronto at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Comparison of agreement in predicted angle by pose-estimation library (time frame: 4 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05366114, which also lists 21 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Nov 2023.