Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
Trial ID: NCT05635006
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-12-31
Completion date: 2026-12-31
Sponsor: Hai Lv
Conditions: Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence
Interventions: [None]
Locations: The fifth affiliated hospital of SYSU
Countries: China
Enrollment: 1132
Age range: None - None
Sex: ALL

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.

Eligibility criteria:
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.

Exclusion criteria: patients with conditions like spondylolysis, lumbar spondylolisthesis, lumbar spine fractures, previous lumbar surgery, or severe scoliosis.

Outcomes:
To compare the accuracy of multitask deep learning model and clinicians in judging spinal facet joint degeneration; To compare the precision of multitask deep learning model and clinicians in judging spinal facet joint degeneration; To compare the sensitivity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration; To compare the specificity of multitask deep learning model and clinicians in assessing spinal facet joint degeneration; Calculate the F1 score for evaluating the severity of facet joints degeneration in the multitask deep learning model; ROC (Receiver Operation Characteristic) is called receiver operation characteristic curve, which is an index to evaluate the performance of deep learning model