Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models

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.

Study Overview

• Status: Recruiting
• Conditions: Lateral Ligament, Ankle
• Intervention / Treatment: Diagnostic test: Diagnositic test

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.

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

• Study Contact: Name: huishu Yuan, MD; Phone Number: 15810245738; Email: huishuy@bjmu.edu.cn
• Study Contact Backup: Name: Ming Ni, MD; Phone Number: 13884794867; Email: sdyingxiang2017@163.com

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 010
      • Recruiting
      • Peking University Third Hospital
      • Contact: Huishu Yuan, Dr; Email: huishuy@bjmu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Normal control group-Grade 0; Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear.	Diagnostic test: 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.	Diagnostic test: 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.	Diagnostic test: Diagnositic test; The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models	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

Collaborators and Investigators

• Sponsor: Peking University Third Hospital
• Investigators: Study Chair: huishu Yuan, MD, Peking University Third Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2021
• Primary Completion (Anticipated): December 30, 2021
• Study Completion (Anticipated): March 30, 2022

Study Registration Dates

• First Submitted: June 28, 2021
• First Submitted That Met QC Criteria: June 29, 2021
• First Posted (Actual): July 8, 2021

Study Record Updates

• Last Update Posted (Actual): July 8, 2021
• Last Update Submitted That Met QC Criteria: June 29, 2021
• Last Verified: June 1, 2021

More Information

Terms related to this study

• Keywords: artificial intelligence; anterior talofibular ligament; sports Injury
• Other Study ID Numbers: M2020460

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No