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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

1132 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-12-31

Study Completion Date

2026-12-31

Brief Summary

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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.

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Detailed Description

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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

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Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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Training group

70% of the participants were randomly divided into training groups to train the learning performance of the machine

Intervention Type OTHER

No special intervention, randomly classify the subjects

Validation group

15% of the participants were randomly divided into validation groups to enhance the learning performance of the machine and avoid over fitting

Intervention Type OTHER

No special intervention, randomly classify the subjects

Test group

15% of the participants were randomly divided into test groups to test the learning performance of the machine and draw research conclusions

Intervention Type OTHER

No special intervention, randomly classify the subjects

Interventions

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No special intervention, randomly classify the subjects

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

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

Eligible Sex

ALL

Accepts Healthy Volunteers

No