SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Superior Labrum From Anterior to Posterior Injuries
• Intervention / Treatment: Diagnostic test: Diagnositic test

Detailed Description

1. 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.
2. 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.
3. 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.
4. 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.

• Study Type: Observational
• Enrollment (Anticipated): 800

Contacts and Locations

• Study Contact: Name: huishu Yuan, MD; Phone Number: 15810245738; Email: huishuy@bjmu.edu.cn
• Study Contact Backup: Name: Ming Ni, MD; Phone Number: +8613884794867; Email: sdyingxiang2017@163.com

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 010
      • Peking University Third Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.

Description

Inclusion Criteria:

1. Without any treatment before imaging examination;
2. MR of the shoulder joint was performed within 3 months before the operation and the image quality was good;
3. Arthroscopic operation was performed in our hospital, and the operation records were complete.

Exclusion Criteria:

1. History of shoulder surgery, tumor, or previous fracture;
2. Unclear image, serious artifact, or incomplete clinical data.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Retrospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Normal control group-Grade 0; Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.	Diagnostic test: Diagnositic test; 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.	Diagnostic test: Diagnositic test; 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.	Diagnostic test: Diagnositic test; The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis	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

Collaborators and Investigators

• Sponsor: Peking University Third Hospital

Study record dates

Study Major Dates

• Study Start (Anticipated): October 1, 2021
• Primary Completion (Anticipated): June 1, 2022
• Study Completion (Anticipated): July 1, 2022

Study Registration Dates

• First Submitted: June 29, 2021
• First Submitted That Met QC Criteria: June 29, 2021
• First Posted (Actual): July 7, 2021

Study Record Updates

• Last Update Posted (Actual): July 7, 2021
• Last Update Submitted That Met QC Criteria: June 29, 2021
• Last Verified: June 1, 2021

More Information

Terms related to this study

• Keywords: artificial intelligence; diagnosis; SLAP
• Additional Relevant MeSH Terms: Wounds and Injuries; Shoulder Injuries
• Other Study ID Numbers: M2020458

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No