Deep Learning of Knee Joint MRI Intelligent Detection
1 other identifier
observational
50,000
1 country
1
Brief 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. 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2021
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2021
CompletedFirst Submitted
Initial submission to the registry
June 27, 2021
CompletedFirst Posted
Study publicly available on registry
July 12, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
May 15, 2022
CompletedJuly 12, 2021
June 1, 2021
12 months
June 27, 2021
July 8, 2021
Conditions
Outcome Measures
Primary Outcomes (3)
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.
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.
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.
2021
Eligibility Criteria
All patients related to sports injuries
You may qualify if:
- ACL-injured patients;
- Follow-up of patients after ACL injury;
- patients with genetic predisposition to ACL injury;
You may not qualify if:
- 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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Peking University Third Hospitallead
- Huashan Hospitalcollaborator
- Shanghai Jiao Tong University Affiliated Sixth People's Hospitalcollaborator
- Chinese PLA General Hospitalcollaborator
- Inner Mongolia People's Hospitalcollaborator
- The First Affiliated Hospital of BaoTou Medical Collegecollaborator
- Fourth Medical Center of PLA General Hospitalcollaborator
- The 8th medical center of chinese PLA general hospitalcollaborator
- Hebei Medical University Third Hospitalcollaborator
- Tianjin Hospitalcollaborator
Study Sites (1)
Institute of Sports Medicine, Peking University Third Hospital
Beijing, Beijing Municipality, 100191, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Lin Lin
Peking University Third Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
June 27, 2021
First Posted
July 12, 2021
Study Start
January 1, 2021
Primary Completion
December 31, 2021
Study Completion
May 15, 2022
Last Updated
July 12, 2021
Record last verified: 2021-06