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Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury

NCT06372873 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2024-04-23

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. Using datasets from multiple clinical centers, the investigators aimed to develop and validate a deep convolutional network (DCNN) model that automates classification of ATFL injuries using US images with the goal of providing interpretable assistance to radiologists and facilitating a more accurate diagnosis of ATFL injuries.

The investigators collected US images of ATFL injuries which had arthroscopic surgery results as reference standard form 13 hospitals across China;Then the investigators divided the images into training dataset, internal validation dataset, and external validation dataset in a ratio of 8:1:1; the investigators chose an optimal DCNN model to test its diagnostic performance of the model, including the diagnostic accuracy, sensitivity, specificity, F1 score. At last, the investigators compared the diagnostic performance of the model with 12 radiologists at different levels of expertise.

Conditions

  • Deep Learning
  • Ultrasound
  • Anterior Talofibular Ligament

Interventions

OTHER

re-evaluate by two senior radiologists in our medical center

The allocated images obtained from the contributing hospitals will be re-evaluated by two senior radiologists in our clinical center

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
2024-04-01
Primary Completion
2024-04-30
Completion
2025-05-30

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 NCT06372873 on ClinicalTrials.gov