Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury

Peking University

Status: Not yet enrolling

Conditions: Deep Learning; Anterior Talofibular Ligament; Ultrasound

Treatments: Other: Ultrasound examination

Study type: Observational

Funder types: Other

Identifiers

NCT06373029

2023PHB211-001(2)

Details and patient eligibility

About

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.

Enrollment: 400 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

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

Trial design

400 participants in 1 patient group

Experimental group

Treatment:

Other: Ultrasound examination