Clinical trial NCT04955067: Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Official title: Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Lateral Ligament, Ankle
Keywords: artificial intelligence
anterior talofibular ligament
sports Injury
Lead sponsor: Peking University Third Hospital
Sponsor class: OTHER
First posted: 2021-07-08
Enrollment: 1000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
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.

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.

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.

Primary outcomes:
measure: Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
timeFrame: 2021.1-2022.3.1
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University Third Hospital
state: Beijing
Study officials:
affiliation: Peking University Third Hospital
name: huishu Yuan, MD
role: STUDY_CHAIR
References: