Carpal Tunnel Syndrome (CTS)NCT06697392
Ultrasound-based Artificial Intelligence for Classification of Carpal Tunnel Syndrome
An observational study of Carpal Tunnel Syndrome (CTS) and Ultrasound, run by Peking University People's Hospital at 1 site in China.
Trial summary
Carpal tunnel syndrome (CTS) is one of the most prevalent peripheral neuropathies, impacting approximately 4% of the general population. It is typically classified into three degrees: mild, moderate, and severe. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Carpal Tunnel Syndrome (CTS), Ultrasound, Artificial Intelligence (AI)
- What is being tested?
- ultrasound examination
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 15 Nov 2024to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The investigators intend to perform ultrasound examinations for patients with idiopathic CTS adhering to specific inclusion and exclusion criteria, aiming to develop and test the efficacy of AI model for CTS grading.
Who may be able to join
- those who have complained about associated symptoms about CTS, including pain, numbness, and weakness of hand.
- those who perform ultrasound examinations of median nerve within 1 week of the symptom.
- those who have electrophysilogical test results as reference standard.
Who cannot join
- those who had a surgery in the affected hand.
- those who had a trauma or fracture in the affected hand.
- those who had rheumatoid-related conditions, autoimmune diseases, and endocrine disorders.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Ultrasound-based Artificial Intelligence for Grading of Carpal Tunnel Syndrome, a Multicenter Study in China
Carpal tunnel syndrome (CTS) is one of the most prevalent peripheral neuropathies, impacting approximately 4% of the general population. It is typically classified into three degrees: mild, moderate, and severe. Accurate grading of carpal tunnel syndrome (CTS) is essential for determining appropriate treatment options, thereby playing a crucial role in optimizing patient outcomes. Electrophysiological testing (EST) is a key parameter for grading carpal tunnel syndrome (CTS). However, it is limited by several factors, including its invasive nature, poor reproducibility, and reduced sensitivity for detecting early-stage disease. Recently, ultrasound has gained widespread acceptance among clinicians for the assessment and grading of CTS. Nonetheless, radiologists often encounter challenges in this process due to the variability in image quality, differences in experience, and inherent subjectivity.
To address these issues, artificial intelligence presents a promising solution. Therefore, this study aims to develop a deep learning model for grading CTS by leveraging multimodal imaging features, including B-mode ultrasound, superb microvascular imaging (SMI), and elastography. Additionally, the investigators intend to validate the model's effectiveness by testing it with images from various clinical centers, ensuring its generalizability across different clinical settings.
- Being tested
- ultrasound examination
- Keywords
- carpal tunnel syndrome, ultrasound, artificial intelligence
- Medical subject headings
- Carpal Tunnel Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ultrasound examinationOther
The investigators intend to perform ultrasound examinations for the participants with CTS.
| Study group | Role | What they receive |
|---|---|---|
| Prospective test set | Not described Receives: Other: ultrasound examination |
What is being measured
The primary outcome is the main question the study is designed to answer.
grading of CTS
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Nov 2024
- Study start15 Nov 2024
- Primary completion30 Jun 2025 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Carpal Tunnel Syndrome (CTS)Studies of the same condition, open and under way.
- More trials run by Peking University People's HospitalThe 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
- Other
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Shi Xiaochen, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06697392
- Sponsor's protocol ID
- 2024PHB019-001(5)
- Status last verified
- Nov 2024
- First submitted
- 17 Nov 2024
- Last updated
- 20 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3-21. doi: 10.1007/978-3-030-33128-3_1.
PubMed 32030660Wielemborek PT, Kapica-Topczewska K, Pogorzelski R, Bartoszuk A, Kochanowicz J, Kulakowska A. Carpal tunnel syndrome conservative treatment: a literature review. Postep Psychiatr Neurol. 2022 Jun;31(2):85-94. doi: 10.5114/ppn.2022.116880. Epub 2022 May 31.
PubMed 37082094Lam KHS, Wu YT, Reeves KD, Galluccio F, Allam AE, Peng PWH. Ultrasound-Guided Interventions for Carpal Tunnel Syndrome: A Systematic Review and Meta-Analyses. Diagnostics (Basel). 2023 Mar 16;13(6):1138. doi: 10.3390/diagnostics13061138.
PubMed 36980446
Study at a glance
From the registry record, last updated by the sponsor on 20 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ultrasound-based Artificial Intelligence for Grading of Carpal Tunnel Syndrome, a Multicenter Study in China
- ClinicalTrials.gov ID
- NCT06697392
- Acronym / sponsor ID
- 2024PHB019-001(5)
- Keywords
- carpal tunnel syndrome, ultrasound, artificial intelligence
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 15 Nov 2024 (actual)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 20 Nov 2024 (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.
Carpal tunnel syndrome (CTS) is one of the most prevalent peripheral neuropathies, impacting approximately 4% of the general population. It is typically classified into three degrees: mild, moderate, and severe. Accurate grading of carpal tunnel syndrome (CTS) is essential for determining appropriate treatment options, thereby playing a crucial role in optimizing patient outcomes. Electrophysiological testing (EST) is a key parameter for grading carpal tunnel syndrome (CTS). However, it is limited by several factors, including its invasive nature, poor reproducibility, and reduced sensitivity for detecting early-stage disease. Recently, ultrasound has gained widespread acceptance among clinicians for the assessment and grading of CTS. Nonetheless, radiologists often encounter challenges in this process due to the variability in image quality, differences in experience, and inherent subjectivity.
To address these issues, artificial intelligence presents a promising solution. Therefore, this study aims to develop a deep learning model for grading CTS by leveraging multimodal imaging features, including B-mode ultrasound, superb microvascular imaging (SMI), and elastography. Additionally, the investigators intend to validate the model's effectiveness by testing it with images from various clinical centers, ensuring its generalizability across different clinical settings.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective test set | – | Other: ultrasound examination |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The investigators intend to perform ultrasound examinations for patients with idiopathic CTS adhering to specific inclusion and exclusion criteria, aiming to develop and test the efficacy of AI model for CTS grading.
Key inclusion criteria (3 listed)
- those who have complained about associated symptoms about CTS, including pain, numbness, and weakness of hand.
- those who perform ultrasound examinations of median nerve within 1 week of the symptom.
- those who have electrophysilogical test results as reference standard.
Key exclusion criteria (3 listed)
- those who had a surgery in the affected hand.
- those who had a trauma or fracture in the affected hand.
- those who had rheumatoid-related conditions, autoimmune diseases, and endocrine disorders.
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 |
|---|---|---|
| ultrasound examinationThe investigators intend to perform ultrasound examinations for the participants with CTS. | Other | Prospective test set |
Primary outcome
grading of CTS
Time frame: baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Nov 2024.
- Background: PubMed 32030660Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3-21. doi: 10.1007/978-3-030-33128-3_1.
- Background: PubMed 37082094Wielemborek PT, Kapica-Topczewska K, Pogorzelski R, Bartoszuk A, Kochanowicz J, Kulakowska A. Carpal tunnel syndrome conservative treatment: a literature review. Postep Psychiatr Neurol. 2022 Jun;31(2):85-94. doi: 10.5114/ppn.2022.116880. Epub 2022 May 31.
- Background: PubMed 36980446Lam KHS, Wu YT, Reeves KD, Galluccio F, Allam AE, Peng PWH. Ultrasound-Guided Interventions for Carpal Tunnel Syndrome: A Systematic Review and Meta-Analyses. Diagnostics (Basel). 2023 Mar 16;13(6):1138. doi: 10.3390/diagnostics13061138.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Carpal Tunnel Syndrome (CTS) and Ultrasound, run by Peking University People's Hospital at 1 site in China.
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 3 inclusion and 3 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: under way, not looking for participants, as of the sponsor's last update on 20 Nov 2024. 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 China. 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?
This study is still under way. Results are not available in the source record yet.
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?
grading of CTS (time frame: baseline). 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 NCT06697392, which also lists 3 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 20 Nov 2024.