Knee InjuriesNCT04958408
Deep Learning of Knee Joint MRI Intelligent Detection
An observational study of Knee Injuries, run by Peking University Third Hospital at 1 site in China.
Trial summary
Knee joint is the most common part of sports injury. MRI is a powerful tool to diagnose knee joint injury. However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed diagnosis. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Knee Injuries
- How many people
- 50,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 15 May 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: All patients related to sports injuries
Who may be able to join
- ACL-injured patients;
- Follow-up of patients after ACL injury;
- patients with genetic predisposition to ACL injury;
Who cannot join
- Patients with joint injury caused by clear external forces;
- Definitely have stroke, heart disease, epilepsy, cranial neurosurgery, migraine;
- Have had a concussion or head injury in the past 6 months.
Trial locations and contacts
1 site in 1 country.
Lin Lin contact
China1 site, 1 recruiting
- Recruiting
Institute of Sports Medicine, Peking University Third Hospital
Beijing, Beijing Municipality, 100191
Jia-kuo Yu, MD, 86-10-82267392, yujiakuo@126.com
Ai-Bing Huang, PhD, 8615650715003, hab165@163.com
About this study
Knee joint is the most common part of sports injury. MRI is a powerful tool to diagnose knee joint injury. However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed diagnosis. The emerging deep learning technology can establish automatic recognition model through large samples. A large sample of knee joint MRI was collected retrospectively to train the deep learning model of knee joint MRI, and the sensitivity and specificity of the deep learning model were verified in multi center. Depending on the clinical needs, the deep learning model annotation system is established. A large number of knee MRI were obtained and labeled. According to the knee joint MRI training depth learning model, and iterative optimization, the final version is formed. Multi center validation was carried out. Continuous operation records and corresponding preoperative knee MRI were obtained from multiple hospitals. The sensitivity and specificity of the model were calculated with operation records as the gold standard. At the same time, an expert team composed of senior radiologists and sports medicine doctors was organized to read the films. The sensitivity and specificity of manual reading and AI reading were compared to prove the superiority of AI reading. This study can improve the efficiency of clinical MRI film reading, reduce the workload of doctors, improve the film reading level of grass-roots hospitals, promote the development of the discipline, and has good social benefits and market prospects.
Read the detailed description
The knee joint is the most common sports injury site in the human body, including ligament rupture, meniscus tear, cartilage lesions, and free body formation. Knee MRI has extremely high sensitivity and specificity in diagnosing knee diseases, especially its negative predictive value is close to 100%, and it is an effective means to assist clinicians in diagnosing knee diseases. However, there are many MRI sequences of the knee joint, and different diseases have different imaging effects on various sequences, and the types of knee joint diseases are complicated, so it takes a long time to evaluate the knee joint MRI. Due to the huge clinical demand for knee MRI, it has caused a great burden on radiology and sports medicine orthopedics. At the same time, for some special injuries of the knee joint, such as hidden meniscus tear, rupture of the anterior cross part and adhesion in place after rupture, local ligament injury, etc., the conclusions given by different readers are very different, and it is easy to miss the diagnosis. And the missed diagnosis seriously affects the prognosis of the knee joint, leading to the progression of arthritis. In addition, professional musculoskeletal system imaging experts have a long training cycle, and a large number of orthopedic doctors and radiologists in basic hospitals have limited reading skills for knee MRI, which limits the development of local sports medicine disciplines and the development of related diagnosis and treatment. The purpose of our research is to train the deep learning model of knee MRI through multi-center and large sample of knee MRI; Multi-center verification of the sensitivity and specificity of the knee MRI deep learning model, and compare the accuracy of the deep learning model and manual image reading.
- Conditions
- Knee Injuries
- Medical subject headings
- Knee Injuries
What is being measured
The primary outcome is the main question the study is designed to answer.
Marking system design based on Magnetic Resonance Imaging（MRI）
According to the development goals, combined with the performance of MRI and the structure of the model algorithm, the labeling rules and logic of knee MRI are determined. On this basis, a labeling system is designed, and different labeling tools are designed for a variety of lesions.
Measured: 2021
Data export and annotation
Encrypt the MRI file and import it into the medical standard intelligent labeling system. Create a dedicated tagging account for each tagger to tag. Based on the previously marked image data, develop algorithms for segmenting different lesion areas.
Measured: 2021
Build a deep learning model
According to the diagnostic logic, we select the coronal and sagittal images of the knee joint T2 MRI sequence for analysis. And choose the Resnext model that has been verified by a large number of ImageNet and other large data sets to extract the features of the coronal out-of-state images. After the multi-layer convolution operation, the key feature representation of the image is extracted. At the same time, in the process of feature extraction, the batch normalization module is used to perform feature transformation to highlight the most meaningful part of the feature.
Measured: 2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jul 2021
- Study start1 Jan 2021
- Primary completion31 Dec 2021 (estimated)
- Study completion15 May 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Knee 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 50,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Huashan Hospital, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Chinese PLA General Hospital, Inner Mongolia People's Hospital, The First Affiliated Hospital of BaoTou Medical College, Fourth Medical Center of PLA General Hospital, Chinese the Eighth Medical Center of the General Hospital of the People's Liberation Army, Hebei Medical University Third Hospital, Tianjin Hospital
- Responsible party
- Sponsor
- Investigators
- Lin Lin, Study director, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04958408
- Sponsor's protocol ID
- M2020243
- Status last verified
- Jun 2021
- First submitted
- 27 Jun 2021
- Last updated
- 12 Jul 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning of Knee Joint MRI Intelligent Detection
- ClinicalTrials.gov ID
- NCT04958408
- Acronym / sponsor ID
- M2020243
- Conditions
- Knee Injuries
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Collaborators
- Huashan Hospital, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Chinese PLA General Hospital, Inner Mongolia People's Hospital, The First Affiliated Hospital of BaoTou Medical College, Fourth Medical Center of PLA General Hospital, Chinese the Eighth Medical Center of the General Hospital of the People's Liberation Army, Hebei Medical University Third Hospital, Tianjin Hospital
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Dec 2021 (estimated)
- Study completion
- 15 May 2022 (estimated)
- First posted
- 12 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.
Knee joint is the most common part of sports injury. MRI is a powerful tool to diagnose knee joint injury. However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed diagnosis. The emerging deep learning technology can establish automatic recognition model through large samples. A large sample of knee joint MRI was collected retrospectively to train the deep learning model of knee joint MRI, and the sensitivity and specificity of the deep learning model were verified in multi center. Depending on the clinical needs, the deep learning model annotation system is established. A large number of knee MRI were obtained and labeled. According to the knee joint MRI training depth learning model, and iterative optimization, the final version is formed. Multi center validation was carried out. Continuous operation records and corresponding preoperative knee MRI were obtained from multiple hospitals. The sensitivity and specificity of the model were calculated with operation records as the gold standard. At the same time, an expert team composed of senior radiologists and sports medicine doctors was organized to read the films. The sensitivity and specificity of manual reading and AI reading were compared to prove the superiority of AI reading. This study can improve the efficiency of clinical MRI film reading, reduce the workload of doctors, improve the film reading level of grass-roots hospitals, promote the development of the discipline, and has good social benefits and market prospects.
Detailed description
The knee joint is the most common sports injury site in the human body, including ligament rupture, meniscus tear, cartilage lesions, and free body formation. Knee MRI has extremely high sensitivity and specificity in diagnosing knee diseases, especially its negative predictive value is close to 100%, and it is an effective means to assist clinicians in diagnosing knee diseases. However, there are many MRI sequences of the knee joint, and different diseases have different imaging effects on various sequences, and the types of knee joint diseases are complicated, so it takes a long time to evaluate the knee joint MRI. Due to the huge clinical demand for knee MRI, it has caused a great burden on radiology and sports medicine orthopedics. At the same time, for some special injuries of the knee joint, such as hidden meniscus tear, rupture of the anterior cross part and adhesion in place after rupture, local ligament injury, etc., the conclusions given by different readers are very different, and it is easy to miss the diagnosis. And the missed diagnosis seriously affects the prognosis of the knee joint, leading to the progression of arthritis. In addition, professional musculoskeletal system imaging experts have a long training cycle, and a large number of orthopedic doctors and radiologists in basic hospitals have limited reading skills for knee MRI, which limits the development of local sports medicine disciplines and the development of related diagnosis and treatment. The purpose of our research is to train the deep learning model of knee MRI through multi-center and large sample of knee MRI; Multi-center verification of the sensitivity and specificity of the knee MRI deep learning model, and compare the accuracy of the deep learning model and manual image reading.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- All patients related to sports injuries
Key inclusion criteria (3 listed)
- ACL-injured patients;
- Follow-up of patients after ACL injury;
- patients with genetic predisposition to ACL injury;
Key exclusion criteria (3 listed)
- Patients with joint injury caused by clear external forces;
- Definitely have stroke, heart disease, epilepsy, cranial neurosurgery, migraine;
- Have had a concussion or head injury in the past 6 months.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Marking system design based on Magnetic Resonance Imaging（MRI）
Time frame: 2021
According to the development goals, combined with the performance of MRI and the structure of the model algorithm, the labeling rules and logic of knee MRI are determined. On this basis, a labeling system is designed, and different labeling tools are designed for a variety of lesions.
Data export and annotation
Time frame: 2021
Encrypt the MRI file and import it into the medical standard intelligent labeling system. Create a dedicated tagging account for each tagger to tag. Based on the previously marked image data, develop algorithms for segmenting different lesion areas.
Build a deep learning model
Time frame: 2021
According to the diagnostic logic, we select the coronal and sagittal images of the knee joint T2 MRI sequence for analysis. And choose the Resnext model that has been verified by a large number of ImageNet and other large data sets to extract the features of the coronal out-of-state images. After the multi-layer convolution operation, the key feature representation of the image is extracted. At the same time, in the process of feature extraction, the batch normalization module is used to perform feature transformation to highlight the most meaningful part of the feature.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jul 2021.
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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.
Lin Lin contact
Lin Lin study director
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 Knee 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 3 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 12 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 Jia-Kuo Yu (01082267392, yujiakuo@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Marking system design based on Magnetic Resonance Imaging（MRI） (time frame: 2021). The study lists 3 primary outcomes in all. 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 12 Jul 2021.