SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
2021年6月29日 更新者：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.
調査の概要
詳細な説明
- 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.
研究の種類
観察的
入学 (予想される)
800
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究連絡先
- 名前：huishu Yuan, MD
- 電話番号：15810245738
- メール：huishuy@bjmu.edu.cn
研究連絡先のバックアップ
- 名前：Ming Ni, MD
- 電話番号：+8613884794867
- メール：sdyingxiang2017@163.com
研究場所
-
-
Beijing
-
Beijing、Beijing、中国、010
- Peking University Third Hospital
-
-
参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
なし
受講資格のある性別
全て
サンプリング方法
非確率サンプル
調査対象母集団
Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.
説明
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.
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
- 観測モデル：ケースコントロール
- 時間の展望：回顧
コホートと介入
グループ/コホート |
介入・治療 |
|---|---|
|
Normal control group-Grade 0
Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.
|
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.
|
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Ligament tear-Grade 2
Arthroscopy of the shoulder revealed partial or complete loss of labrum.
|
The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
時間枠：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
|
協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (予想される)
2021年10月1日
一次修了 (予想される)
2022年6月1日
研究の完了 (予想される)
2022年7月1日
試験登録日
最初に提出
2021年6月29日
QC基準を満たした最初の提出物
2021年6月29日
最初の投稿 (実際)
2021年7月7日
学習記録の更新
投稿された最後の更新 (実際)
2021年7月7日
QC基準を満たした最後の更新が送信されました
2021年6月29日
最終確認日
2021年6月1日
詳しくは
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。