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AI-Based Localization and Screening of Sinus Tarsi Abnormalities on Ankle MRI
Sponsor: The Third Affiliated Hospital of Southern Medical University
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. 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.
Official title: Lightweight Automatic Localization and Abnormality Screening of the Sinus Tarsi Based on Single-Sequence Ankle MRI: A Multicenter Study
Key Details
Gender
All
Age Range
18 Years - 50 Years
Study Type
OBSERVATIONAL
Enrollment
240
Start Date
2026-01-01
Completion Date
2026-10-07
Last Updated
2026-10-14
Healthy Volunteers
Yes
Conditions
Interventions
Standalone Radiologist Interpretation
Radiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance.
AI-assisted Radiologist Interpretation
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.
Locations (1)
The Third Affiliated Hospital of Southern Medical University
Guangzhou, Guangdong, China