Superior Labrum From Anterior to Posterior InjuriesNCT04953026
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
An observational study of Superior Labrum From Anterior to Posterior Injuries, run by Peking University Third Hospital at 1 site in China.
Trial summary
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Superior Labrum From Anterior to Posterior Injuries
- What is being tested?
- Diagnositic test
- How many people
- 800 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2021to 1 Jul 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
- Child, Adult, Older adult
Study population: Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.
Who may be able to join
- Without any treatment before imaging examination;
- MR of the shoulder joint was performed within 3 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 shoulder 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
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.
Read the detailed description
- Recognition of labrum images based on LeNet: axial and oblique coronal T2-fs images were used, and all images were corrected and standardized. LeNet identified the images with labrum of the shoulder joint, and the images with labrum structure of shoulder joint were selected from the complete sequence. In contrast, the images without labrum structure were deleted. 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 glenoid lip of shoulder joint based on DenseNet: the labrum is recognized by DenseNet in the selected image. The labelimg software based on Python was used to locate the labrum coordinates and then input them into Python for recognition learning. All the data were divided into a training set (70% and 30% of the training set were selected as the verification set). 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 and improve the recognition efficiency and accuracy. Finally, input the result to the next step.
- Recognition and grading of shoulder SLAP injury based on 3D-CNN: recognition and grading of input data through 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 is divided into a training set (70%, and then 30% of the training set is selected as the verification set), and the remaining 30% is used as the test set to evaluate the classification accuracy to identify whether there is labrum injury and grade the image with injury.
- Establish CNN combined model: after establishing the model for the axial and oblique coronal view according to the above process (1-3), according to the output characteristics of the CNN classification model, predict the probability of different grades before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combined model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final joint model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the joint model.
- Being tested
- Diagnositic test
- Keywords
- artificial intelligence, SLAP, diagnosis
- Medical subject headings
- Shoulder Injuries, Disease
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 shoulder 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 labrum was normal, and the labrum was intact without injury or tear. Receives: Diagnostic Test: Diagnositic test | |
| Ligament injury -Grade 1 | Arthroscopic examination of the shoulder showed labrum degeneration or injury, but no local or complete tear. Receives: Diagnostic Test: Diagnositic test | |
| Ligament tear-Grade 2 | Arthroscopy of the shoulder revealed partial or complete loss of labrum. Receives: Diagnostic Test: Diagnositic test |
What is being measured
The primary outcome is the main question the study is designed to answer.
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
The model of deep learning was obtained for diagnosis and grading of SLAP injury and compared with the radiologists of different stages.
Measured: 2021.10.1-2022.7.1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jul 2021
- Study start1 Oct 2021 (estimated)
- Primary completion1 Jun 2022 (estimated)
- Study completion1 Jul 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Superior Labrum From Anterior to Posterior InjuriesStudies 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
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04953026
- Sponsor's protocol ID
- M2020458
- Status last verified
- Jun 2021
- First submitted
- 29 Jun 2021
- Last updated
- 7 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 7 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
- ClinicalTrials.gov ID
- NCT04953026
- Acronym / sponsor ID
- M2020458
- Keywords
- artificial intelligence, SLAP, diagnosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Oct 2021 (estimated)
- Primary completion
- 1 Jun 2022 (estimated)
- Study completion
- 1 Jul 2022 (estimated)
- First posted
- 7 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.
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.
Detailed description
- Recognition of labrum images based on LeNet: axial and oblique coronal T2-fs images were used, and all images were corrected and standardized. LeNet identified the images with labrum of the shoulder joint, and the images with labrum structure of shoulder joint were selected from the complete sequence. In contrast, the images without labrum structure were deleted. 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 glenoid lip of shoulder joint based on DenseNet: the labrum is recognized by DenseNet in the selected image. The labelimg software based on Python was used to locate the labrum coordinates and then input them into Python for recognition learning. All the data were divided into a training set (70% and 30% of the training set were selected as the verification set). 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 and improve the recognition efficiency and accuracy. Finally, input the result to the next step.
- Recognition and grading of shoulder SLAP injury based on 3D-CNN: recognition and grading of input data through 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 is divided into a training set (70%, and then 30% of the training set is selected as the verification set), and the remaining 30% is used as the test set to evaluate the classification accuracy to identify whether there is labrum injury and grade the image with injury.
- Establish CNN combined model: after establishing the model for the axial and oblique coronal view according to the above process (1-3), according to the output characteristics of the CNN classification model, predict the probability of different grades before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combined model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final joint model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the joint model.
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 labrum was normal, and the labrum was intact without injury or tear. |
| Ligament injury -Grade 1 | – | Diagnostic Test: Diagnositic testArthroscopic examination of the shoulder showed labrum degeneration or injury, but no local or complete tear. |
| Ligament tear-Grade 2 | – | Diagnostic Test: Diagnositic testArthroscopy of the shoulder revealed partial or complete loss of labrum. |
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.
Key inclusion criteria (3 listed)
- Without any treatment before imaging examination;
- MR of the shoulder joint was performed within 3 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 shoulder 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 |
|---|---|---|
| Diagnositic testThe results of shoulder 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
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
Time frame: 2021.10.1-2022.7.1
The model of deep learning was obtained for diagnosis and grading of SLAP injury and compared with the radiologists of different stages.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2021.
- Individual participant dataSharing planned: No
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.
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 Superior Labrum From Anterior to Posterior Injuries, 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 7 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?
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis (time frame: 2021.10.1-2022.7.1). 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 7 Jul 2021.