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Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury

NCT06373029 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2024-04-18

No results posted yet for this study

Summary

Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. The investigators have already developed a deep convolutional network (DCNN) model that automates detailed classification of ATFL injuries. The investigators hope to use the DCNN in real-world clinical setting to test its diagnostic accuracy.

Conditions

  • Ultrasound
  • Anterior Talofibular Ligament
  • Deep Learning

Interventions

OTHER

Ultrasound examination

The investigators made ultrasound examinations to the participants to test whether the model could improve their diagnostic accuracy

Sponsors & Collaborators

  • Peking University People's Hospital

    lead OTHER

Principal Investigators

  • Jiaan Zhu, Dr · Peking University People's Hospital

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-20
Primary Completion
2024-05-30
Completion
2025-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06373029 on ClinicalTrials.gov