Sinus Tarsi AbnormalityNCT07873411
AI-Based Localization and Screening of Sinus Tarsi Abnormalities on Ankle MRI
An observational study of Sinus Tarsi Abnormality, run by The Third Affiliated Hospital of Southern Medical University at 1 site in China.
Trial summary
This study constructs a lightweight AI screening workflow based on sagittal fat-suppressed ankle MRI for automatic localization and normal-abnormal classification of sinus tarsi. With multi-slice feature fusion, it compares performances of different input strategies and lightweight networks. Read more about this study
- Who is it for?
- Ages 18 to 50 yearsHealthy volunteers can take part
- Conditions the study is for
- Sinus Tarsi Abnormality
- What is being tested?
- Standalone Radiologist Interpretation and AI-assisted Radiologist Interpretation
- How many people
- 240 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2026to 7 Oct 2026
- 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 to 50 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Patients from multiple medical centers in China who underwent ankle MRI examinations due to ankle injury, pain or discomfort, representing patients with both normal and abnormal sinus tarsi findings.
Who may be able to join
- Ankle MRI for injury, pain, or discomfort;
- Available sagittal fat-suppressed MRI;
- Basic clinical information available;
- Adequate image quality.
Who cannot join
- Previous ankle surgery or internal fixation with major postoperative changes;
- Tumor, infection, or severe deformity
- Severe motion or metal artifacts, or incomplete field of view;
- Missing key MRI sequences;
- Missing essential clinical information.
Trial locations and contacts
1 site in 1 country.
China1 site
The Third Affiliated Hospital of Southern Medical University
Guangzhou, Guangdong, 510630
About this study
Official title: Lightweight Automatic Localization and Abnormality Screening of the Sinus Tarsi Based on Single-Sequence Ankle MRI: A Multicenter Study
This study constructs a lightweight AI screening workflow based on sagittal fat-suppressed ankle MRI for automatic localization and normal-abnormal classification of sinus tarsi. With multi-slice feature fusion, it compares performances of different input strategies and lightweight networks. Multi-center datasets are used to validate model generalization. An MRMC (multi-reader multi-case) study is conducted to compare diagnostic performance between the model and radiologists. A web-based verification workflow is developed, and CPU-based runtime performance is evaluated to explore its clinical potential.
Read the detailed description
Based on sagittal fat-suppressed MRI of the ankle, this study builds a lightweight artificial intelligence-assisted screening workflow to realize automatic localization and normal-abnormal classification of the sinus tarsi region. It adopts automatic target localization to eliminate redundant background information and integrates local features of continuous MRI images via multi-slice feature fusion, while comparing the screening performance of different image input strategies and lightweight deep learning networks. Internal and external independent multi-center datasets are utilized to validate the stability and generalization ability of the model. Furthermore, a rigorous MRMC (multi-reader multi-case) study with eight radiology readers was performed on a total of 240 MRI examinations. All readers evaluated the full dataset in two independent reading sessions separated by a 28-day washout period. In the first session, each reader randomly interpreted 120 cases with AI assistance and the remaining 120 cases without AI assistance. In the second session, the AI assistance status of all cases was crossover-swapped, enabling each examination to be assessed by every reader under both AI-assisted and unassisted conditions. Meanwhile, a web-based visual verification workflow was established, and the operational efficiency and practical feasibility of the model under ordinary CPU environments were evaluated, aiming to explore the clinical application value of the proposed AI-assisted ankle MRI screening scheme.
- Conditions
- Sinus Tarsi Abnormality
- Being tested
- Standalone Radiologist Interpretation, AI-assisted Radiologist Interpretation
- Keywords
- Deep Learning, Multicenter Study, Lightweighting
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Standalone Radiologist InterpretationOther
Radiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance.
AI-assisted Radiologist InterpretationOther
Radiologists adopt the proposed artificial intelligence model for auxiliary interpretation of the same set of medical images to quantitatively evaluate the improvement in diagnostic performance brought by the model.
| Study group | Role | What they receive |
|---|---|---|
| Validation Cohort | Retrospective observational validation cohorts were established using retrospective ankle MRI data collected from six independent centers after model locking for observational validation, with eligible cases included in the randomized crossover MRMC reader study. Receives: Other: Standalone Radiologist Interpretation; Other: AI-assisted Radiologist Interpretation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Examination-level Sensitivity and Area Under the ROC Curve (AUC)
Evaluation of examination-level sensitivity (defined as the proportion of true-positive examinations among all examinations with positive reference-standard labels) and discrimination performance (measured by AUC) to compare unaided radiologist performance versus AI-assisted performance.
Measured: Up to 1 week per evaluation period
Secondary outcomes (2)
Diagnostic Efficiency (Reading and Reporting Time)
Measurement of diagnostic efficiency recorded as the time (in seconds) taken by radiologists to complete the case review and generate findings, with and without AI assistance.
Measured: Up to 1 week per evaluation period
Examination-level Specificity and Accuracy
Evaluation of examination-level specificity (defined as the proportion of true-negative examinations among all examinations with negative reference-standard labels) and accuracy (defined as the proportion of diagnostic decisions matching the reference-standard label) to compare unaided radiologist performance versus AI-assisted performance.
Measured: Up to 1 week per evaluation period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Oct 2026
- Study start1 Jan 2026
- Primary completion25 Sep 2026
- Study completion7 Oct 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Sinus Tarsi AbnormalityStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 240 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Third Affiliated Hospital of Southern Medical University
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Junyan Li, The Third Affiliated Hospital of Southern Medical University
- Investigators
- Yinghua Zhao, Principal investigator, The Third Affiliated Hospital of Southern Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07873411
- Sponsor's protocol ID
- 2026-ER-118
- Status last verified
- Oct 2026
- First submitted
- 7 Oct 2026
- Last updated
- 14 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Lightweight Automatic Localization and Abnormality Screening of the Sinus Tarsi Based on Single-Sequence Ankle MRI: A Multicenter Study
- ClinicalTrials.gov ID
- NCT07873411
- Acronym / sponsor ID
- 2026-ER-118
- Conditions
- Sinus Tarsi Abnormality
- Keywords
- Deep Learning, Multicenter Study, Lightweighting
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 240 participants (actual)
- Lead sponsor
- The Third Affiliated Hospital of Southern Medical UniversityOther government
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 25 Sep 2026 (actual)
- Study completion
- 7 Oct 2026 (actual)
- First posted
- 14 Oct 2026 (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.
This study constructs a lightweight AI screening workflow based on sagittal fat-suppressed ankle MRI for automatic localization and normal-abnormal classification of sinus tarsi. With multi-slice feature fusion, it compares performances of different input strategies and lightweight networks. Multi-center datasets are used to validate model generalization. An MRMC (multi-reader multi-case) study is conducted to compare diagnostic performance between the model and radiologists. A web-based verification workflow is developed, and CPU-based runtime performance is evaluated to explore its clinical potential.
Detailed description
Based on sagittal fat-suppressed MRI of the ankle, this study builds a lightweight artificial intelligence-assisted screening workflow to realize automatic localization and normal-abnormal classification of the sinus tarsi region. It adopts automatic target localization to eliminate redundant background information and integrates local features of continuous MRI images via multi-slice feature fusion, while comparing the screening performance of different image input strategies and lightweight deep learning networks. Internal and external independent multi-center datasets are utilized to validate the stability and generalization ability of the model. Furthermore, a rigorous MRMC (multi-reader multi-case) study with eight radiology readers was performed on a total of 240 MRI examinations. All readers evaluated the full dataset in two independent reading sessions separated by a 28-day washout period. In the first session, each reader randomly interpreted 120 cases with AI assistance and the remaining 120 cases without AI assistance. In the second session, the AI assistance status of all cases was crossover-swapped, enabling each examination to be assessed by every reader under both AI-assisted and unassisted conditions. Meanwhile, a web-based visual verification workflow was established, and the operational efficiency and practical feasibility of the model under ordinary CPU environments were evaluated, aiming to explore the clinical application value of the proposed AI-assisted ankle MRI screening scheme.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Validation Cohort | – | Other: Standalone Radiologist Interpretation, Other: AI-assisted Radiologist InterpretationRetrospective observational validation cohorts were established using retrospective ankle MRI data collected from six independent centers after model locking for observational validation, with eligible cases included in the randomized crossover MRMC reader study. |
Who may qualify
- Ages
- 18 to 50 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Patients from multiple medical centers in China who underwent ankle MRI examinations due to ankle injury, pain or discomfort, representing patients with both normal and abnormal sinus tarsi findings.
Key inclusion criteria (4 listed)
- Ankle MRI for injury, pain, or discomfort;
- Available sagittal fat-suppressed MRI;
- Basic clinical information available;
- Adequate image quality.
Key exclusion criteria (5 listed)
- Previous ankle surgery or internal fixation with major postoperative changes;
- Tumor, infection, or severe deformity
- Severe motion or metal artifacts, or incomplete field of view;
- Missing key MRI sequences;
- Missing essential clinical information.
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 |
|---|---|---|
| Standalone Radiologist InterpretationRadiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance. | Other | Validation Cohort |
| AI-assisted Radiologist InterpretationRadiologists adopt the proposed artificial intelligence model for auxiliary interpretation of the same set of medical images to quantitatively evaluate the improvement in diagnostic performance brought by the model. | Other | Validation Cohort |
Primary outcome
Examination-level Sensitivity and Area Under the ROC Curve (AUC)
Time frame: Up to 1 week per evaluation period
Evaluation of examination-level sensitivity (defined as the proportion of true-positive examinations among all examinations with positive reference-standard labels) and discrimination performance (measured by AUC) to compare unaided radiologist performance versus AI-assisted performance.
2 secondary outcomes
Diagnostic Efficiency (Reading and Reporting Time)
Time frame: Up to 1 week per evaluation period
Examination-level Specificity and Accuracy
Time frame: Up to 1 week per evaluation period
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Oct 2026.
- Individual participant dataSharing planned: No
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.
Yinghua Zhao principal investigator
The Third Affiliated Hospital of Southern Medical 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 Sinus Tarsi Abnormality, run by The Third Affiliated Hospital of Southern Medical University 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 5 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: finished, as of the sponsor's last update on 14 Oct 2026. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Examination-level Sensitivity and Area Under the ROC Curve (AUC) (time frame: Up to 1 week per evaluation period). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 14 Oct 2026.