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Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models

2021年6月29日 更新者：Peking University Third Hospital
The purpose of this study is to study the injury of the anterior talofibular ligament by deep learning method and compare a variety of different deep learning models to establish a deep learning method that can accurately identify and grade the injury of anterior talofibular ligament, and obtain a model with better recognition and grading effect.

研究概览

地位

招聘中

条件

干预/治疗

详细说明

  1. Recognition and segmentation of anterior talofibular ligament based on DenseNet. Densenet was used to recognize the axial T2-fs image, and the image level was the most typical one. The labelimg program based on Python was used to locate the coordinates of the anterior talofibular ligament and then imported into Python for learning. All the data were divided into a training set (70%, and then 30% of the training set was selected as the verification set). The remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the anterior talofibular ligament, the local clipping and amplification are carried out to remove the redundant information. Finally, input the result to the next step.
  2. Establishment and comparison of various deep learning models: four deep learning models were established and compared in this study, namely VGG19, AlexNet, CapsNet, and GoogleNet. The models using image fitting alone and those combining with clinical physical examination data were compared for each deep learning model. The diagnostic efficiency between models was expressed by the ROC curve, including AUC, F1 score, etc. the ROC curve was further analyzed by t-test, Delong test, and other statistical methods. In this study, the data were divided into a training set (70%, 30% in the training set as the validation set), and the remaining 30% as the test set to evaluate the classification accuracy.

研究类型

观察性的

注册 (预期的)

1000

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

研究联系人备份

学习地点

  • 中国
    • Beijing
      • Beijing、Beijing、中国、010
        • 招聘中
        • Peking University Third Hospital
        • 接触：

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 孩子
  • 成人
  • 年长者

接受健康志愿者

是的

有资格学习的性别

全部

取样方法

非概率样本

研究人群

From September 2018 to September 2020, patients underwent ankle MRI examination in the Department of Radiology, the Third Hospital of Peking University.

描述

Inclusion Criteria:

  1. Without any treatment before imaging examination;
  2. MR of ankle joint was performed within 3 months before operation and the image quality was good;
  3. Arthroscopic operation was performed in our hospital and the operation records were complete.

Exclusion Criteria:

  1. history of ankle surgery, history of cancer or previous fractures.
  2. Unclear image, serious artifact or incomplete clinical data.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

队列和干预

团体/队列
干预/治疗
Normal control group-Grade 0
Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.
诊断测试：Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
Ligament injury -Grade 1
Arthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear.
诊断测试：Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
Ligament tear-Grade 2
Arthroscopy of the ankle joint revealed partial or complete loss of ligaments.
诊断测试：Diagnositic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
大体时间：2021.1-2022.3.1
The model of deep learning was obtained for diagnosis and grading of anterior fibular ligament and compared with the doctors of different grades.
2021.1-2022.3.1

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Peking University Third Hospital

调查人员

  • 学习椅：huishu Yuan, MD、Peking University Third Hospital

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2021年1月1日

初级完成 (预期的)

2021年12月30日

研究完成 (预期的)

2022年3月30日

研究注册日期

首次提交

2021年6月28日

首先提交符合 QC 标准的

2021年6月29日

首次发布 (实际的)

2021年7月8日

研究记录更新

最后更新发布 (实际的)

2021年7月8日

上次提交的符合 QC 标准的更新

2021年6月29日

最后验证

2021年6月1日

更多信息

与本研究相关的术语

关键字

其他研究编号

  • M2020460

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

Diagnositic test的临床试验

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赞助者和合作者

医疗条件

药物干预

CROs by country

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条件

罕见疾病

药物干预

膳食补充剂

赞助者/合作者

地点

3
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