Facet Joints; Degeneration ; Deep Learning ;Artificial IntelligenceNCT05635006

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

An observational study of Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence, run by Hai Lv at 1 site in China.

Active, not recruiting

Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record

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Trial summary

Who is it for?: Any age
Conditions the study is for: Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence
How many people: 1,132 participants
Where: 1 siteChina
Study dates: 31 Dec 2022to 31 Dec 2026
Phase: Observational

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

Ages Any age
All sexes
No healthy volunteers
Child, Adult, Older adult

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.

Who may be able to join

From 2020 to 2023, data of patients receiving lumbar imaging examination in the Fifth Affiliated Hospital of Sun Yat sen University and other hospital.
Exclusion criteria: patients with conditions like spondylolysis, lumbar spondylolisthesis, lumbar spine fractures, previous lumbar surgery, or severe scoliosis.

Who cannot join

None listed.

Trial locations and contacts

1 site in 1 country.

China1 site

The fifth affiliated hospital of SYSU
Zhuhai, Guangdong

Other trials in China

About this study

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.

Read the detailed description

This project is a retrospective clinical study. From 2020 to 2022, DICOM-format images and basic information of X-ray, CT, and magnetic resonance (MR) images were collected from outpatients and inpatients with suspected low back pain at the Fifth Affiliated Hospital of Sun Yat-sen University. After obtaining the DICOM image mode, data were exported from the information module upon the successful submission of OA batches; basic patient information was collected from inpatient medical records.

This study plans to include 1,132 patients from a single center, who will be randomly divided into a training set, a validation set, and a test set according to the proportion for automatic diagnosis by the computer deep learning model, aiming to test the stability and reliability of the model. Among these 1,132 patients, two doctors separately conducted graded image reading for joint stenosis, hypertrophy, osteophytes, articular surface erosion, and subchondral cysts. Controversial results were determined by another more experienced doctor, and results of the reference standard group were confirmed by the senior doctor group. The data analysis methods for other centers were consistent with those described above.

By comparing the diagnostic results of clinicians and the model, the performance and clinical feasibility of the deep learning model for the automatic diagnosis of lumbar facet joint degeneration were evaluated. The doctors' judgment results were compared with the model's prediction results, and statistical analysis was performed on the performance of the model's automatic diagnosis. Performance evaluation indicators included accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC value. Among them, F1 score and AUC value are the main indicators for the comprehensive evaluation of model performance; the higher their values, the stronger the model performance.

Conditions: Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Other
No special intervention, randomly classify the subjects

Study groups in NCT05635006
Study group	Role	What they receive
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 being measured

The primary outcome is the main question the study is designed to answer.

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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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.
Measured: 2022.12.01-2023.07.31

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

31 Dec 202231 Dec 2026

First posted2 Dec 2022
Study start31 Dec 2022
Primary completion31 Dec 2026 (estimated)
Study completion31 Dec 2026 (estimated)

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Other trials for Facet Joints; Degeneration ; Deep Learning ;Artificial IntelligenceStudies of the same condition, open and under way.
What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design

Study type: Observational
Observational model: Other
Time perspective: Retrospective
Enrolment: 1,132 participants (estimated)

IDs, dates and publications

ClinicalTrials.gov ID: NCT05635006
Sponsor's protocol ID: ZDWY.JZWK.004
Status last verified: Aug 2025
First submitted: 23 Nov 2022
Last updated: 13 Aug 2025
Results posted: No
Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: No: If necessary, it can be provided