SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
2021년 6월 29일 업데이트: Peking University Third Hospital
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.
연구 개요
상태
아직 모집하지 않음
개입 / 치료
상세 설명
- 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.
연구 유형
관찰
등록 (예상)
800
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 연락처
- 이름: huishu Yuan, MD
- 전화번호: 15810245738
- 이메일: huishuy@bjmu.edu.cn
연구 연락처 백업
- 이름: Ming Ni, MD
- 전화번호: +8613884794867
- 이메일: sdyingxiang2017@163.com
연구 장소
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Beijing
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Beijing, Beijing, 중국, 010
- Peking University Third Hospital
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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
Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.
설명
Inclusion Criteria:
- 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.
Exclusion Criteria:
- History of shoulder surgery, tumor, or previous fracture;
- Unclear image, serious artifact, or incomplete clinical data.
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: 케이스 컨트롤
- 시간 관점: 회고전
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
|---|---|
|
Normal control group-Grade 0
Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.
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The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
|
Ligament injury -Grade 1
Arthroscopic examination of the shoulder showed labrum degeneration or injury, but no local or complete tear.
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The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
|
Ligament tear-Grade 2
Arthroscopy of the shoulder revealed partial or complete loss of labrum.
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The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
기간: 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.
|
2021.10.1-2022.7.1
|
공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
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 (예상)
2021년 10월 1일
기본 완료 (예상)
2022년 6월 1일
연구 완료 (예상)
2022년 7월 1일
연구 등록 날짜
최초 제출
2021년 6월 29일
QC 기준을 충족하는 최초 제출
2021년 6월 29일
처음 게시됨 (실제)
2021년 7월 7일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2021년 7월 7일
QC 기준을 충족하는 마지막 업데이트 제출
2021년 6월 29일
마지막으로 확인됨
2021년 6월 1일
추가 정보
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