Labrum Injury of the Hip JointNCT04950036
Research on Acetabular Labrum Injury Based on MR: Multi-angle Deep Learning Model
An observational study of Labrum Injury of the Hip Joint, run by Peking University Third Hospital at 1 site in China.
Trial summary
The purpose of this study is to study the MRI images of acetabular labrum injury by deep learning method, and try to establish a combination model of axial and coronal serial images, and compare with the diagnostic accuracy of radiologists, to establish a deep learning method for accurate identification and… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Labrum Injury of the Hip Joint
- What is being tested?
- Diagnostic Test
- How many people
- 1,261 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2020to 30 May 2021
- 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, MRI was performed 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 the hip joint was performed within three months before the 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 hip surgery, tumor, or previous fracture;
- Unclear image, serious artifact, or incomplete clinical data.
Trial locations and contacts
1 site in 1 country.
About this study
The purpose of this study is to study the MRI images of acetabular labrum injury by deep learning method, and try to establish a combination model of axial and coronal serial images, and compare with the diagnostic accuracy of radiologists, to establish a deep learning method for accurate identification and classification of acetabular labrum injury.
Read the detailed description
- Detection of acetabular labrum images based on CNN: axial and coronal T2-fs images were used, and all images were corrected and standardized. CNN is applied to recognize and learn the images with acetabular labrum to select the images with acetabular labrum structure from the complete sequence and delete the images without acetabular labrum structure. All the data are divided into a training set (70%, 30% in training set as verification set), and the remaining 30% as a test set to evaluate the accuracy of model recognition. Enter the obtained results into the next step.
- Recognition and segmentation of acetabular labrum based on Densenet: using Densenet to recognize and learn the acetabular labrum from the selected images. LabelImg was used to locate the acetabular labrum coordinates manually and then input them into Python for recognition learning. All the data were divided into a training set (70%, and then 30% in the training set was selected as the verification set), and the remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the labrum structure, the labrum structure is locally cut and enlarged to remove the redundant information. Finally, input the result to the next step.
- Identification and grading of acetabular labrum injury based on 3D-CNN: the input data were identified and graded by the 3D-CNN model. 3D-CNN is divided into eight layers: input layer, hard wire layer H1, convolution layer C2, downsampling layer S3, convolution layer C4, downsampling layer S5, convolution layer C6 and output layer. 3D-CNN constructs a cube by stacking multiple consecutive frames and then uses a 3D convolution kernel in the cube. Through this structure, the feature images in the convolution layer will be connected with multiple adjacent frames in the previous layer to realize the information acquisition of continuous images. Similarly, the data were divided into a training set (70%, and then 30% of the training set was selected as the verification set), and the remaining 30% was used as the test set to evaluate the classification accuracy to identify the injury of the labrum and classify the cases with injury.
- Combination model: according to the above process (1-3), after the models are established for the axial and coronal images respectively, according to the output characteristics of the CNN classification model, the probabilities of different grades are predicted before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combination model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the model.
- Comparison of radiologists and deep learning: List the test set cases in the above step 3 and ask two MSK professional radiologists to classify whether there is damage and the degree of damage, and compared with the results with artificial diagnosis (both doctors read the images independently and obtained the diagnosis results by simulating the normal state of writing the report without any prompt). Finally, the accuracy of artificial diagnosis was compared with that of the combination model obtained in the fourth step.
- Conditions
- Labrum Injury of the Hip Joint
- Being tested
- Diagnostic Test
- Keywords
- artificial intelligence, Labrum injury, dignosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic 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 hip was normal, and the labrum was intact without injury or tear. Receives: Diagnostic Test: Diagnostic Test | |
| Ligament injury -Grade 1 | Arthroscopic examination of the hip showed labrum degeneration or injury, but no local or complete tear. Receives: Diagnostic Test: Diagnostic Test | |
| Ligament tear-Grade 2 | Arthroscopy of the hip revealed partial or complete loss of labrum. Receives: Diagnostic Test: Diagnostic Test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Research on acetabular labrum injury based on MR: multi-angle deep learning model
The model of deep learning was obtained for diagnosis and grading of labrum injury and compared with the doctors of different stages.
Measured: 2020.12.01-2021.05.30
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jul 2021
- Study start1 Dec 2020
- Primary completion30 Apr 2021
- Study completion30 May 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Labrum Injury of the Hip JointStudies of the same condition, open and under way.
- 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
- 1,261 participants (actual)
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
- NCT04950036
- Sponsor's protocol ID
- M2020459
- Status last verified
- Jun 2021
- First submitted
- 29 Jun 2021
- Last updated
- 2 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 2 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Acetabular Labrum Injury Based on MR: Multi-angle Deep Learning Model
- ClinicalTrials.gov ID
- NCT04950036
- Acronym / sponsor ID
- M2020459
- Conditions
- Labrum Injury of the Hip Joint
- Keywords
- artificial intelligence, Labrum injury, dignosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,261 participants (actual)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Dec 2020 (actual)
- Primary completion
- 30 Apr 2021 (actual)
- Study completion
- 30 May 2021 (actual)
- First posted
- 2 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 MRI images of acetabular labrum injury by deep learning method, and try to establish a combination model of axial and coronal serial images, and compare with the diagnostic accuracy of radiologists, to establish a deep learning method for accurate identification and classification of acetabular labrum injury.
Detailed description
- Detection of acetabular labrum images based on CNN: axial and coronal T2-fs images were used, and all images were corrected and standardized. CNN is applied to recognize and learn the images with acetabular labrum to select the images with acetabular labrum structure from the complete sequence and delete the images without acetabular labrum structure. All the data are divided into a training set (70%, 30% in training set as verification set), and the remaining 30% as a test set to evaluate the accuracy of model recognition. Enter the obtained results into the next step.
- Recognition and segmentation of acetabular labrum based on Densenet: using Densenet to recognize and learn the acetabular labrum from the selected images. LabelImg was used to locate the acetabular labrum coordinates manually and then input them into Python for recognition learning. All the data were divided into a training set (70%, and then 30% in the training set was selected as the verification set), and the remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the labrum structure, the labrum structure is locally cut and enlarged to remove the redundant information. Finally, input the result to the next step.
- Identification and grading of acetabular labrum injury based on 3D-CNN: the input data were identified and graded by the 3D-CNN model. 3D-CNN is divided into eight layers: input layer, hard wire layer H1, convolution layer C2, downsampling layer S3, convolution layer C4, downsampling layer S5, convolution layer C6 and output layer. 3D-CNN constructs a cube by stacking multiple consecutive frames and then uses a 3D convolution kernel in the cube. Through this structure, the feature images in the convolution layer will be connected with multiple adjacent frames in the previous layer to realize the information acquisition of continuous images. Similarly, the data were divided into a training set (70%, and then 30% of the training set was selected as the verification set), and the remaining 30% was used as the test set to evaluate the classification accuracy to identify the injury of the labrum and classify the cases with injury.
- Combination model: according to the above process (1-3), after the models are established for the axial and coronal images respectively, according to the output characteristics of the CNN classification model, the probabilities of different grades are predicted before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combination model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the model.
- Comparison of radiologists and deep learning: List the test set cases in the above step 3 and ask two MSK professional radiologists to classify whether there is damage and the degree of damage, and compared with the results with artificial diagnosis (both doctors read the images independently and obtained the diagnosis results by simulating the normal state of writing the report without any prompt). Finally, the accuracy of artificial diagnosis was compared with that of the combination model obtained in the fourth step.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Normal control group-Grade 0 | – | Diagnostic Test: Diagnostic TestArthroscopic examination of the hip was normal, and the labrum was intact without injury or tear. |
| Ligament injury -Grade 1 | – | Diagnostic Test: Diagnostic TestArthroscopic examination of the hip showed labrum degeneration or injury, but no local or complete tear. |
| Ligament tear-Grade 2 | – | Diagnostic Test: Diagnostic TestArthroscopy of the hip revealed partial or complete loss of labrum. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- From September 2018 to September 2020, MRI was performed in the Department of Radiology, the Third Hospital of Peking University.
Key inclusion criteria (3 listed)
- Without any treatment before imaging examination;
- MR of the hip joint was performed within three months before the 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 hip surgery, tumor, or previous fracture;
- 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 |
|---|---|---|
| Diagnostic 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
Research on acetabular labrum injury based on MR: multi-angle deep learning model
Time frame: 2020.12.01-2021.05.30
The model of deep learning was obtained for diagnosis and grading of labrum injury and compared with the doctors of different stages.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 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 Labrum Injury of the Hip Joint, 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: finished, as of the sponsor's last update on 2 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Research on acetabular labrum injury based on MR: multi-angle deep learning model (time frame: 2020.12.01-2021.05.30). 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 2 Jul 2021; AzTrials last refreshed its copy on 30 Oct 2026.