- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05635006
Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
December 19, 2022 updated by: Hai Lv
Spinal facet joint osteoarthritis is a disease with high incidence among people over 40 years old.
It is a disease characterized by a series of degenerative pathological changes and clinical features of synovium, articular cartilage, subchondral bone, joint space and accessory tissues of spinal facet joints under the action of multiple factors.
Some physiological or pathological factors can lead to osteoarthritis of spinal facet joints.
Patients with spinal facet osteoarthritis often have different degrees of clinical manifestations such as back pain and dyskinesia, which significantly affect the physical and mental health of patients.
The severity of spinal facet osteoarthritis not only has a certain impact on low back pain and changes in low back muscle density, but also affects patient management and treatment plan.
At present, different doctors have certain subjectivity in the grading reading of lumbar facet osteoarthritis, and the consistency and repeatability of the results are poor.
Moreover, doctors need to read image images and judge the grading is very time-consuming and repetitive work.
In recent years, the application of deep learning technology in medical image analysis has been widely concerned by clinicians.
Deep learning has great potential benefits in medical imaging diagnosis.
It can provide semi-automatic reports under the supervision of radiologists, so as to improve the accuracy, consistency, objectivity and rapidity of disease degree assessment, and further support clinical decision-making on this basis.
This project plans to develop an intelligent diagnosis and classification system for degenerative diseases of small joints of the spine with multi task and in-depth learning, and verify its clinical feasibility, aiming to help clinicians improve the accuracy, consistency, objectivity and rapidity of the corresponding disease degree evaluation, and further support the follow-up clinical decision-making.
Study Overview
Status
Not yet recruiting
Intervention / Treatment
Detailed Description
This project is a retrospective clinical study.
from 2019 to 2022, the DICOM format images and basic information of imaging X ray, CT, and MR of outpatient and inpatients suspected of low back and in the Fifth Affiliated Hospital of Sun Yat sen University were collected.
Get DICOM image mode, and the information section will export the data after the OA batch is successfully submitted; Basic patient information is collected from inpatient medical history.
700 patients are planned to be included.
Those projects are randomly divided into training set, verification set and test set according to the ratio of 7:1.5:1.5 for automatic diagnosis of computer in-depth learning model to test the stability and reliability of the model.
In 700 projects, two junior doctors and two senior doctors with ten years of film reading experience respectively took grading readings of joint stenosis, hypertrophy, osteophyte, articular surface erosion and subchondral cyst.
The inconsistent results in the junior doctors group were jointly decided by the two doctors.
The reference standard group was decided by the senior doctors group.
By comparing the diagnosis results of clinicians and models, To evaluate the performance and clinical feasibility of the deep learning model for automatic diagnosis of lumbar facet degeneration.Compare the results of doctor's judgment and model prediction, and statistically analyze the performance of automatic diagnosis of the model.
Performance evaluation indicators include accuracy, sensitivity, specificity, accuracy, recall, F1 value and AUC value.
F1 value and AUC value are the main indicators for comprehensive evaluation of model performance.
The higher F1 value and AUC value, the stronger the model performance.
Study Type
Observational
Enrollment (Anticipated)
700
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Wang YunFei, doctor
- Phone Number: 18845791883
- Email: jack11314159@qq.com
Study Contact Backup
- Name: Lv Hai, Chief physician;doctor
- Phone Number: 18665000036
- Email: 18665000036@qq.com
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Sampling Method
Non-Probability Sample
Study Population
For patients receiving imaging examination due to low back pain, the degree of degeneration of the facet joints of the patients is no to severe.
Remove patients meeting the exclusion criteria to avoid poor image quality affecting judgment.
Description
Inclusion Criteria:
- From 2019 to 2022, data of patients receiving lumbar imaging examination in the Fifth Affiliated Hospital of Sun Yat sen University
Exclusion Criteria:
- Lumbar spondylolisthesis
- Previous vertebral fracture
- Have a history of vertebral surgery
- Severe artifacts at lumbar images
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Other
- Time Perspectives: Retrospective
Cohorts and Interventions
Group / Cohort |
---|
Training group
70% of the participants were randomly divided into training groups to train the learning performance of the machine
|
Validation group
15% of the participants were randomly divided into validation groups to enhance the learning performance of the machine and avoid over fitting
|
Test group
15% of the participants were randomly divided into test groups to test the learning performance of the machine and draw research conclusions
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
To compare the accuracy of multitask deep learning model and clinicians in judging spinal facet joint degeneration
Time Frame: 2022.12.01-2023.07.31
|
It is mainly used to indicate the number of correctly predicted samples in the total number of samples.True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Accuracy = (TP + TN) / (TP + FN + FP +TN)
|
2022.12.01-2023.07.31
|
To compare the precision of multitask deep learning model and clinicians in judging spinal facet joint degeneration
Time Frame: 2022.12.01-2023.07.31
|
True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Precision = TP / (TP+FP)
|
2022.12.01-2023.07.31
|
To compare the sensitivity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration
Time Frame: 2022.12.01-2023.07.31
|
True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Sensitivity=TP / (TP+FN)
|
2022.12.01-2023.07.31
|
To compare the specificity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration
Time Frame: 2022.12.01-2023.07.31
|
True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Specificity=TN / (TN+FP)
|
2022.12.01-2023.07.31
|
Calculate the F1 score for evaluating the severity of facet joints degeneration in the multitask deep learning model
Time Frame: 2022.12.01-2023.07.31
|
F1 score is an important evaluation indicator for automatic classification,F1 =2*Precision*Sensitivity/(Precision+Sensitivity)=2TP/(2TP+FP+FN)
|
2022.12.01-2023.07.31
|
ROC (Receiver Operation Characteristic) is called receiver operation characteristic curve, which is an index to evaluate the performance of deep learning model
Time Frame: 2022.12.01-2023.07.31
|
ROC (Receiver Operation Characteristic) is called receiver operation characteristic curve.
The closer the curve is to the upper left corner, the better the classifier is.
The area under the ROC curve is called AUC.
The larger the AUC is, the better the classification effect of the classifier will be.
|
2022.12.01-2023.07.31
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Anticipated)
December 31, 2022
Primary Completion (Anticipated)
January 31, 2023
Study Completion (Anticipated)
January 31, 2023
Study Registration Dates
First Submitted
November 23, 2022
First Submitted That Met QC Criteria
December 1, 2022
First Posted (Actual)
December 2, 2022
Study Record Updates
Last Update Posted (Actual)
December 20, 2022
Last Update Submitted That Met QC Criteria
December 19, 2022
Last Verified
December 1, 2022
More Information
Terms related to this study
Other Study ID Numbers
- ZDWY.JZWK.004
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
No
IPD Plan Description
If necessary, it can be provided
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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