Ignite Creation Date:
2025-12-26 @ 2:41 PM
Ignite Modification Date:
2025-12-26 @ 2:41 PM
Study NCT ID:
NCT05635006
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-08-13
First Post:
2022-11-23
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1132}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2022-12-31', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-08', 'studyFirstSubmitDate': '2022-11-23', 'studyFirstSubmitQcDate': '2022-12-01', 'lastUpdatePostDateStruct': {'date': '2025-08-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-12-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'To compare the accuracy of multitask deep learning model and clinicians in judging spinal facet joint degeneration', 'timeFrame': '2022.12.01-2023.07.31', 'description': '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)'}, {'measure': 'To compare the precision of multitask deep learning model and clinicians in judging spinal facet joint degeneration', 'timeFrame': '2022.12.01-2023.07.31', 'description': 'True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Precision = TP / (TP+FP)'}, {'measure': 'To compare the sensitivity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration', 'timeFrame': '2022.12.01-2023.07.31', 'description': 'True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Sensitivity=TP / (TP+FN)'}, {'measure': 'To compare the specificity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration', 'timeFrame': '2022.12.01-2023.07.31', 'description': 'True Positive(TP),False Negative(FN), False Positive(FP),True Negative(TN).Specificity=TN / (TN+FP)'}, {'measure': 'Calculate the F1 score for evaluating the severity of facet joints degeneration in the multitask deep learning model', 'timeFrame': '2022.12.01-2023.07.31', 'description': 'F1 score is an important evaluation indicator for automatic classification,F1 =2\\*Precision\\*Sensitivity/（Precision+Sensitivity）=2TP/(2TP+FP+FN)'}, {'measure': 'ROC (Receiver Operation Characteristic) is called receiver operation characteristic curve, which is an index to evaluate the performance of deep learning model', 'timeFrame': '2022.12.01-2023.07.31', 'description': '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.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': "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.\n\nThis 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.\n\nBy 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n\\- From 2020 to 2023, data of patients receiving lumbar imaging examination in the Fifth Affiliated Hospital of Sun Yat sen University and other hospital.\n\nExclusion criteria: patients with conditions like spondylolysis, lumbar spondylolisthesis, lumbar spine fractures, previous lumbar surgery, or severe scoliosis.'}, 'identificationModule': {'nctId': 'NCT05635006', 'briefTitle': 'Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration', 'organization': {'class': 'OTHER', 'fullName': 'Fifth Affiliated Hospital, Sun Yat-Sen University'}, 'officialTitle': 'Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration', 'orgStudyIdInfo': {'id': 'ZDWY.JZWK.004'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training group', 'description': '70% of the participants were randomly divided into training groups to train the learning performance of the machine'}, {'label': 'Validation group', 'description': '15% of the participants were randomly divided into validation groups to enhance the learning performance of the machine and avoid over fitting'}, {'label': 'Test group', 'description': '15% of the participants were randomly divided into test groups to test the learning performance of the machine and draw research conclusions'}], 'interventions': [{'type': 'OTHER', 'description': 'No special intervention, randomly classify the subjects', 'armGroupLabels': ['Test group', 'Training group', 'Validation group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Zhuhai', 'state': 'Guangdong', 'country': 'China', 'facility': 'The fifth affiliated hospital of SYSU', 'geoPoint': {'lat': 22.27694, 'lon': 113.56778}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'If necessary, it can be provided'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hai Lv', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Chief physician', 'investigatorFullName': 'Hai Lv', 'investigatorAffiliation': 'Fifth Affiliated Hospital, Sun Yat-Sen University'}}}}