Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury
Not yet recruiting
- Conditions
- UltrasoundAnterior Talofibular LigamentDeep Learning
- Interventions
- Other: Ultrasound examination
- Registration Number
- NCT06373029
- Lead Sponsor
- Peking University People's Hospital
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 400
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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Experimental group Ultrasound examination -
- Primary Outcome Measures
Name Time Method classification of ATFL injury Baseline ultrasound classification of ATFL injury versus surgery results
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Peking University People's Hospital
🇨🇳Beijing, Beijing, China