Deep Learning of Knee Joint MRI Intelligent Detection
2021년 7월 8일 업데이트: Peking University Third Hospital
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.
연구 개요
상태
모병
정황
상세 설명
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.
연구 유형
관찰
등록 (예상)
50000
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 연락처
- 이름: Jia-Kuo Yu
- 전화번호: 01082267392
- 이메일: yujiakuo@126.com
연구 장소
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Beijing
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Beijing, Beijing, 중국, 100191
- 모병
- Institute of Sports Medicine, Peking University Third Hospital
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연락하다:
- Ai-Bing Huang, PhD
- 전화번호: 8615650715003
- 이메일: hab165@163.com
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
해당 없음
연구 대상 성별
모두
샘플링 방법
비확률 샘플
연구 인구
All patients related to sports injuries
설명
Inclusion Criteria:
- ACL-injured patients;
- Follow-up of patients after ACL injury;
- patients with genetic predisposition to ACL injury;
Exclusion Criteria:
- 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.
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: 보병대
- 시간 관점: 회고전
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Marking system design based on Magnetic Resonance Imaging（MRI）
기간: 2021
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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.
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2021
|
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Data export and annotation
기간: 2021
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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.
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2021
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Build a deep learning model
기간: 2021
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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.
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2021
|
공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
협력자
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (실제)
2021년 1월 1일
기본 완료 (예상)
2021년 12월 31일
연구 완료 (예상)
2022년 5월 15일
연구 등록 날짜
최초 제출
2021년 6월 27일
QC 기준을 충족하는 최초 제출
2021년 7월 8일
처음 게시됨 (실제)
2021년 7월 12일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2021년 7월 12일
QC 기준을 충족하는 마지막 업데이트 제출
2021년 7월 8일
마지막으로 확인됨
2021년 6월 1일
추가 정보
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