NCT06373029

Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury

Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China: A Prospective, Multicentre, Diagnostic Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 400 (estimated)
• Sponsor: Peking University People's Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: —

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.

Conditions

• Ultrasound
• Anterior Talofibular Ligament
• Deep Learning

Interventions

Type	Name	Description
OTHER	Ultrasound examination	The investigators made ultrasound examinations to the participants to test whether the model could improve their diagnostic accuracy

Timeline

• Start date: 2024-04-20
• Primary completion: 2024-05-30
• Completion: 2025-12-30
• First posted: 2024-04-18
• Last updated: 2024-04-18

Locations

1 site across 1 country: China