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

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.

This project is a retrospective clinical study. from 2019 to 2022, the DICOM format images and basic information of imaging X ray, CT, and MR of outpatient and inpatients suspected of low back and in the Fifth Affiliated Hospital of Sun Yat sen University were collected. Get DICOM image mode, and the information section will export the data after the OA batch is successfully submitted; Basic patient information is collected from inpatient medical history. 700 patients are planned to be included. Those projects are randomly divided into training set, verification set and test set according to the ratio of 7:1.5:1.5 for automatic diagnosis of computer in-depth learning model to test the stability and reliability of the model. In 700 projects, two junior doctors and two senior doctors with ten years of film reading experience respectively took grading readings of joint stenosis, hypertrophy, osteophyte, articular surface erosion and subchondral cyst. The inconsistent results in the junior doctors group were jointly decided by the two doctors. The reference standard group was decided by the senior doctors group. By comparing the diagnosis results of clinicians and models, To evaluate the performance and clinical feasibility of the deep learning model for automatic diagnosis of lumbar facet degeneration.Compare the results of doctor's judgment and model prediction, and statistically analyze the performance of automatic diagnosis of the model. Performance evaluation indicators include accuracy, sensitivity, specificity, accuracy, recall, F1 value and AUC value. F1 value and AUC value are the main indicators for comprehensive evaluation of model performance. The higher F1 value and AUC value, the stronger the model performance.

Eligibility Criteria:
Inclusion Criteria:

- From 2019 to 2022, data of patients receiving lumbar imaging examination in the Fifth Affiliated Hospital of Sun Yat sen University

Exclusion Criteria:

* Lumbar spondylolisthesis
* Previous vertebral fracture
* Have a history of vertebral surgery
* Severe artifacts at lumbar images