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临床试验/NCT06373029
NCT06373029尚未招募不适用

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

Peking University People's Hospital1 个研究点 分布在 1 个国家目标入组 400 人开始时间: 2024年4月20日最近更新:
适应症

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Zhu Jiaan

Chairman

Peking University People's Hospital

研究点 (1)

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