Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration

NCT05635006 | Active Not Recruiting

• 1 other identifier: ZDWY.JZWK.004
• Study Type: observational
• Target: 1,132
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence

Outcome Measures

Primary Outcomes (6)

• To compare the accuracy of multitask deep learning model and clinicians in judging spinal facet joint degeneration

  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

  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

  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

  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

  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

  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

Study Arms (3)

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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• \- From 2020 to 2023, data of patients receiving lumbar imaging examination in the Fifth Affiliated Hospital of Sun Yat sen University and other hospital.

Sponsors & Collaborators

• Hai Lv: lead

Study Sites (1)

The fifth affiliated hospital of SYSU

Zhuhai, Guangdong, China

Location

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Chief physician

Study Record Dates

• First Submitted: November 23, 2022
• First Posted: December 2, 2022
• Study Start: December 31, 2022
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: August 13, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)