Lateral Ligament, AnkleNCT04955067
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
An observational study of Lateral Ligament, Ankle, run by Peking University Third Hospital at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Lateral Ligament, Ankle
- What is being tested?
- Diagnositic test
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 30 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: From September 2018 to September 2020, patients underwent ankle MRI examination in the Department of Radiology, the Third Hospital of Peking University.
Who may be able to join
- 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.
Who cannot join
- history of ankle surgery, history of cancer or previous fractures.
- Unclear image, serious artifact or incomplete clinical data.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University Third Hospital
Beijing, Beijing Municipality, 010
Huishu Yuan, Dr, huishuy@bjmu.edu.cn
About this study
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.
Read the detailed description
- 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.
- Conditions
- Lateral Ligament, Ankle
- Being tested
- Diagnositic test
- Keywords
- artificial intelligence, anterior talofibular ligament, sports Injury
- Medical subject headings
- Athletic Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnositic testDiagnostic test
The results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object
| Study group | Role | What they receive |
|---|---|---|
| Normal control group-Grade 0 | Arthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear. Receives: Diagnostic Test: Diagnositic test | |
| Ligament injury -Grade 1 | Arthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear. Receives: Diagnostic Test: Diagnositic test | |
| Ligament tear-Grade 2 | Arthroscopy of the ankle joint revealed partial or complete loss of ligaments. Receives: Diagnostic Test: Diagnositic test |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2021.1-2022.3.1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2021
- Study start1 Jan 2021
- Primary completion30 Dec 2021 (estimated)
- Study completion30 Mar 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lateral Ligament, AnkleStudies of the same condition, open and under way.
- More trials run by Peking University Third HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- huishu Yuan, MD, Study chair, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04955067
- Sponsor's protocol ID
- M2020460
- Status last verified
- Jun 2021
- First submitted
- 28 Jun 2021
- Last updated
- 8 Jul 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
- ClinicalTrials.gov ID
- NCT04955067
- Acronym / sponsor ID
- M2020460
- Conditions
- Lateral Ligament, Ankle
- Keywords
- artificial intelligence, anterior talofibular ligament, sports Injury
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 30 Dec 2021 (estimated)
- Study completion
- 30 Mar 2022 (estimated)
- First posted
- 8 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- 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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Normal control group-Grade 0 | – | Diagnostic Test: Diagnositic testArthroscopic examination of the ankle joint was normal, and the ligament was intact without injury or tear. |
| Ligament injury -Grade 1 | – | Diagnostic Test: Diagnositic testArthroscopic examination of the ankle joint showed ligament degeneration or injury, but no local or complete tear. |
| Ligament tear-Grade 2 | – | Diagnostic Test: Diagnositic testArthroscopy of the ankle joint revealed partial or complete loss of ligaments. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- From September 2018 to September 2020, patients underwent ankle MRI examination in the Department of Radiology, the Third Hospital of Peking University.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- history of ankle surgery, history of cancer or previous fractures.
- Unclear image, serious artifact or incomplete clinical data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Diagnositic testThe results of hip arthroscopy were taken as the gold standard, and MRI examination was taken as the research object | Diagnostic test | Ligament injury -Grade 1, Ligament tear-Grade 2, Normal control group-Grade 0 |
Primary outcome
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
Time frame: 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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jul 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
huishu Yuan, MD study chair
Peking University Third Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lateral Ligament, Ankle, run by Peking University Third Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact huishu Yuan, MD (15810245738, huishuy@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models (time frame: 2021.1-2022.3.1). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jul 2021.