Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China: A Prospective, Multicentre, Diagnostic Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 400
- 试验地点
- 1
- 主要终点
- classification of ATFL injury
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •age> 18 years old
- •patients who underwent an acute ankle sprain
- •patients with a surgery results of the sprained ankle
排除标准
- •age< 18 years old
- •patients with a previous history of ankle surgery
- •patients with ankle tumors
- •patients with a previous history of rheumatoid arthritis
结局指标
主要结局
classification of ATFL injury
时间窗: Baseline
ultrasound classification of ATFL injury versus surgery results
次要结局
未报告次要终点
研究者
Zhu Jiaan
Chairman
Peking University People's Hospital
