Anterior Talofibular Ligament, Deep Learning, Ultrasound
Conditions
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.
Interventions
OTHERUltrasound examination
The investigators made ultrasound examinations to the participants to test whether the model could improve their diagnostic accuracy
Sponsors
Peking University People's Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| classification of ATFL injury | Baseline | ultrasound classification of ATFL injury versus surgery results |
Countries
China
Contacts
Primary ContactJiaan Zhu, Dr
Outcome results
None listed