NCT04955067Unknown不适用
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
适应症
试验速览
- 阶段
- 不适用
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
研究概览
简要总结
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.
详细描述
- 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.
- 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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Without any treatment before imaging examination;
- •MR of ankle joint was performed within 3 months before operation and the image quality was good;
- •Arthroscopic operation was performed in our hospital and the operation records were complete.
排除标准
- •history of ankle surgery, history of cancer or previous fractures.
- •Unclear image, serious artifact or incomplete clinical data.
结局指标
主要结局
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
时间窗: 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.
次要结局
未报告次要终点
研究者
研究点 (1)
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