EducationNCT06883669
The Application of Artificial Intelligence in Wrist and Hand Joint Ultrasound
An observational study of Education and Joint Diseases, run by West China Hospital at 1 site in China.
Trial summary
1. To develop an AI system that can automatically identify standard sections and save images during wrist and hand joint ultrasound scans, while labeling key anatomical structures. 2. Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Education, Joint Diseases
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 1 Jun 2025
- 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 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: healthy individuals and rheumatoid arthritis (RA) patients，sonographers untrained for MSK ultrasound
Who may be able to join
- Healthy volunteers with good compliance
- No history of disease on peripheral nerve, muscle, or tendons;
- No early - stage RA or history of RA.
- 1. Sonographers with at least 2 years of ultrasound scanning experience
Who cannot join
- 1. Amputees or those with limb disabilities.
- Individuals with poor compliance.
- Research Subjects-for AI system validation
- 1. Those who have performed musculoskeletal ultrasound scanning or received related training
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Xinyi Tang
Chengdu, Sichuan
Xinyi Tang, Dr., +8615680819215
About this study
Official title: Establishment of an Artificial Intelligence Recognition System for Wrist and Hand Joint Ultrasound Images/Videos and Its Assistance to Untrained Sonographers
- To develop an AI system that can automatically identify standard sections and save images during wrist and hand joint ultrasound scans, while labeling key anatomical structures.
- To recruit sonographers untrained in musculoskeletal ultrasound, train them in wrist and hand joint scans, and compare their scanning speed and image quality when using and not using the AI system.
Read the detailed description
Research Background Musculoskeletal ultrasound, a rapidly evolving technique for ultrasound - based diagnosis and treatment of the musculoskeletal system, has seen expanding applications in visualizing peripheral nerves, muscles, tendons, joints, and skin thanks to improved ultrasound resolution. It offers advantages like convenience, cost - effectiveness, safety, real - time dynamics, continuous follow - up, and fast reporting. Given the high incidence and wide prevalence of rheumatic and immunological diseases, high - frequency ultrasound is gaining clinical attention. Consequently, learning and promoting musculoskeletal ultrasound is clinically valuable and necessary. However, this field faces challenges such as operator dependence, slow skill improvement and subjective differences in ultrasound diagnosis criteria and assessment methods for rheumatic diseases. The emergence of intelligent tools (AI) can address the urgent need for fast, accurate, and standardized ultrasound diagnosis. While AI has been used in multiple ultrasound sub - specialties, its application in musculoskeletal ultrasound is limited. This study aims to develop an AI - assisted musculoskeletal ultrasound examination system. It will help ultrasonographers by real - time segmenting structures like muscles, tendons, bone cortex, peripheral nerves, joint spaces, and blood vessels, extracting lesions (e.g., synovitis, tenosynovitis, bone erosion, cartilage destruction), and assessing lesion severity, thereby improving diagnostic accuracy and efficiency. Additionally, the system will shorten the learning cycle, enhance learning efficiency for musculoskeletal ultrasound, and accelerate its adoption in hospitals at all levels.
Research Objectives Primary Objective: To develop a musculoskeletal ultrasound AI - assisted examination system. This system will identify standard examination sections and continuous dynamic images of wrist and hand joints, mark specific structures in real - time, extract lesions, and assess their severity, enhancing examination efficiency and accuracy.
Secondary Objective: To help ultrasonographers shorten their learning period and master musculoskeletal ultrasound examination skills more quickly through the application of the AI - assisted system.
- Conditions
- Education, Joint Diseases
- Keywords
- ultrasound, hand, medical education, artificial intelligence, deep learning, wrist
- Medical subject headings
- Joint Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Research Subjects-for AI system establishment | Collect ultrasound images of wrist and hand joints, annotate and segment them frame - by - frame for model training. | |
| Research Subjects-for AI system validation | Recruit sonographers untrained in musculoskeletal ultrasound, train them in wrist and hand joint scans, and compare their scanning speed and image quality when using and not using the AI system. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Image acquisition is complete
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Mar 2025
- Study start1 Jan 2021
- Primary completion1 May 2025 (estimated)
- Study completion1 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for EducationStudies of the same condition, open and under way.
- More trials run by West China 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- West China Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xinyi Tang, West China Hospital
- Investigators
- Xinyi Tang, Principal investigator, Department of Medical Ultrasound, West China Hospital, Sichuan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06883669
- Sponsor's protocol ID
- 2021(1010)
- Status last verified
- Mar 2025
- First submitted
- 11 Mar 2025
- Last updated
- 4 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Tang X, Yang Y, Zhang L, Wang D, Duan Y, Jiang D, Liu H, Ye F, Zhou C, Tang X, Zhou Y, Qiu X, Wei X, Shen S, Chen J, Liu Y, Han X, Zeng J, Wu R, Qiang W, Cheng S, Yu M, Li Z, Xiao X, Meng G, Luo Y, Qiu L. AI-enhanced Wrist-Hand US Image Acquisition: Development and Initial Clinical Evaluation. Radiology. 2026 Jun;319(3):e251268. doi: 10.1148/radiol.251268.
PubMed 42262241
Study at a glance
From the registry record, last updated by the sponsor on 4 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishment of an Artificial Intelligence Recognition System for Wrist and Hand Joint Ultrasound Images/Videos and Its Assistance to Untrained Sonographers
- ClinicalTrials.gov ID
- NCT06883669
- Acronym / sponsor ID
- 2021(1010)
- Conditions
- Education, Joint Diseases
- Keywords
- ultrasound, hand, medical education, artificial intelligence, deep learning, wrist
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- West China HospitalUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 1 May 2025 (estimated)
- Study completion
- 1 Jun 2025 (estimated)
- First posted
- 19 Mar 2025 (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.
- To develop an AI system that can automatically identify standard sections and save images during wrist and hand joint ultrasound scans, while labeling key anatomical structures.
- To recruit sonographers untrained in musculoskeletal ultrasound, train them in wrist and hand joint scans, and compare their scanning speed and image quality when using and not using the AI system.
Detailed description
Research Background Musculoskeletal ultrasound, a rapidly evolving technique for ultrasound - based diagnosis and treatment of the musculoskeletal system, has seen expanding applications in visualizing peripheral nerves, muscles, tendons, joints, and skin thanks to improved ultrasound resolution. It offers advantages like convenience, cost - effectiveness, safety, real - time dynamics, continuous follow - up, and fast reporting. Given the high incidence and wide prevalence of rheumatic and immunological diseases, high - frequency ultrasound is gaining clinical attention. Consequently, learning and promoting musculoskeletal ultrasound is clinically valuable and necessary. However, this field faces challenges such as operator dependence, slow skill improvement and subjective differences in ultrasound diagnosis criteria and assessment methods for rheumatic diseases. The emergence of intelligent tools (AI) can address the urgent need for fast, accurate, and standardized ultrasound diagnosis. While AI has been used in multiple ultrasound sub - specialties, its application in musculoskeletal ultrasound is limited. This study aims to develop an AI - assisted musculoskeletal ultrasound examination system. It will help ultrasonographers by real - time segmenting structures like muscles, tendons, bone cortex, peripheral nerves, joint spaces, and blood vessels, extracting lesions (e.g., synovitis, tenosynovitis, bone erosion, cartilage destruction), and assessing lesion severity, thereby improving diagnostic accuracy and efficiency. Additionally, the system will shorten the learning cycle, enhance learning efficiency for musculoskeletal ultrasound, and accelerate its adoption in hospitals at all levels.
Research Objectives Primary Objective: To develop a musculoskeletal ultrasound AI - assisted examination system. This system will identify standard examination sections and continuous dynamic images of wrist and hand joints, mark specific structures in real - time, extract lesions, and assess their severity, enhancing examination efficiency and accuracy.
Secondary Objective: To help ultrasonographers shorten their learning period and master musculoskeletal ultrasound examination skills more quickly through the application of the AI - assisted system.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Research Subjects-for AI system establishment | – | –Collect ultrasound images of wrist and hand joints, annotate and segment them frame - by - frame for model training. |
| Research Subjects-for AI system validation | – | –Recruit sonographers untrained in musculoskeletal ultrasound, train them in wrist and hand joint scans, and compare their scanning speed and image quality when using and not using the AI system. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- healthy individuals and rheumatoid arthritis (RA) patients，sonographers untrained for MSK ultrasound
Key inclusion criteria (4 listed)
- Healthy volunteers with good compliance
- No history of disease on peripheral nerve, muscle, or tendons;
- No early - stage RA or history of RA.
- 1. Sonographers with at least 2 years of ultrasound scanning experience
Key exclusion criteria (4 listed)
- 1. Amputees or those with limb disabilities.
- Individuals with poor compliance.
- Research Subjects-for AI system validation
- 1. Those who have performed musculoskeletal ultrasound scanning or received related training
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Image acquisition is complete
Time frame: Day 1
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 4 Apr 2025.
- Derived from this study: PubMed 42262241Tang X, Yang Y, Zhang L, Wang D, Duan Y, Jiang D, Liu H, Ye F, Zhou C, Tang X, Zhou Y, Qiu X, Wei X, Shen S, Chen J, Liu Y, Han X, Zeng J, Wu R, Qiang W, Cheng S, Yu M, Li Z, Xiao X, Meng G, Luo Y, Qiu L. AI-enhanced Wrist-Hand US Image Acquisition: Development and Initial Clinical Evaluation. Radiology. 2026 Jun;319(3):e251268. doi: 10.1148/radiol.251268.
- 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.
Xinyi Tang principal investigator
Department of Medical Ultrasound, West China Hospital, Sichuan University
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 Education and Joint Diseases, run by West China 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 4 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 4 Apr 2025. 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?
Contact Xinyi Tang (+8615680819215, tangxinyi1996@outlook.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Image acquisition is complete (time frame: Day 1). 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 NCT06883669, which also lists 1 linked publication.
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 4 Apr 2025.