Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-04-20

Study Completion Date

2025-12-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Ultrasound Anterior Talofibular Ligament Deep Learning

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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Experimental group

Ultrasound examination

Intervention Type OTHER

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

Interventions

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Ultrasound examination

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

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* age\> 18 years old
* patients who underwent an acute ankle sprain
* patients with a surgery results of the sprained ankle

Exclusion Criteria

* age\< 18 years old
* patients with a previous history of ankle surgery
* patients with ankle tumors
* patients with a previous history of rheumatoid arthritis

Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No