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.
Trial 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… Read more about this study
- 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
- Are there results?
- Not yet: the study is under way
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.
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.
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 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.
- 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)
Sponsor and investigators
- Lead sponsor
- Hai Lv
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hai Lv, Fifth Affiliated Hospital, Sun Yat-Sen University
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
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
- ClinicalTrials.gov ID
- NCT05635006
- Acronym / sponsor ID
- ZDWY.JZWK.004
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,132 participants (estimated)
- Lead sponsor
- Hai LvUniversity, hospital or other
- Study start
- 31 Dec 2022 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 2 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- 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.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No special intervention, randomly classify the subjects | Other | Test group, Training group, Validation group |
Primary outcome
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)
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)
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)
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)
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)
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2025.
- Individual participant dataSharing planned: No. If necessary, it can be provided
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence, run by Hai Lv at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 13 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Aug 2025.