Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models

Peking University

Status: Unknown

Conditions: Lateral Ligament, Ankle

Treatments: Diagnostic Test: Diagnositic test

Study type: Observational

Funder types: Other

Identifiers

NCT04955067

M2020460

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 1,000 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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.

Trial design

1,000 participants in 3 patient groups

Normal control group-Grade 0

Description:

Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.

Treatment:

Diagnostic Test: Diagnositic test

Ligament injury -Grade 1

Description:

Arthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear.

Treatment:

Diagnostic Test: Diagnositic test

Ligament tear-Grade 2

Description:

Arthroscopy of the ankle joint revealed partial or complete loss of ligaments.

Treatment:

Diagnostic Test: Diagnositic test