Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models

NCT ID: NCT04955067

Last Updated: 2021-07-08

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-01-01
• Study Completion Date: 2022-03-30

Brief Summary

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.

Detailed 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.

Conditions

Lateral Ligament, Ankle

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

Normal control group-Grade 0

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

Diagnositic test

Intervention Type: DIAGNOSTIC_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.

Diagnositic test

Intervention Type: DIAGNOSTIC_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.

Diagnositic test

Intervention Type: DIAGNOSTIC_TEST

The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object

Interventions

Diagnositic test

The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Peking University Third Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

huishu Yuan, MD

Role: STUDY_CHAIR

Peking University Third Hospital

Locations

Peking University Third Hospital

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

huishu Yuan, MD

Role: CONTACT

huishuy@bjmu.edu.cn

15810245738

Ming Ni, MD

Role: CONTACT

sdyingxiang2017@163.com

13884794867

Facility Contacts

Huishu Yuan, Dr

Role: primary

huishuy@bjmu.edu.cn

Other Identifiers

M2020460

Identifier Type: -

Identifier Source: org_study_id