Deep Learning-Based Denoised MRI Images for Correlation Analysis between Lumbar Facet Joint and Lumbar Disc Herniation in Spine Surgery

Impact of fixed-point rotational reduction of the spine on pain scores and lower limb motor function in patients with lumbar facet joint disorder

OBJECTIVE

This study explores the relieving effect of spinal fixed-point rotation reduction method on pain scores of patients with lumbar facet joint disorder, and analyzes its impact on lower limb motor function.

METHODS

Ninety patients diagnosed with lumbar facet joint disorder and admitted to our hospital between February 2022 and February 2024 were selected for this study. Patients were randomly divided into two groups: the intervention group and the control group, each comprising 45 individuals. Patients in the intervention group received treatment involving spinal column fixed-point and rotary reduction. Meanwhile, patients in the control group underwent conventional massage, acupuncture, and traction. The pain score, lumbar Japanese Orthopaedic Association scores (JOA) score, balance ability, 10-meter walk test score, stand walk timing test (TUGT), holder walking function grading, and daily living ability score (ADL) were compared before and after treatment.

RESULTS

Compared to pre-treatment values, both groups showed a significant decrease in VAS values at the beginning and 2 weeks after treatment (P < 0.05). Additionally, the treatment group had significantly lower VAS values than the control group at these time points (P < 0.05). Furthermore, during the two-week follow-up period, both groups exhibited a significant increase in lumbar JOA score, balance ability, 10-meter walking test score, TUGT performance time duration, holder walking function grade, and ADL scores compared to pre-treatment values (P < 0.05). At the two-week follow-up assessment, the fixed point rotation reduction group demonstrated significantly higher JOA scores, balance ability, scores on 10-meter walking tests, TUGT performance times, daily living abilities, and holder walking function grades compared to those in the control group(P < 0.05).

CONCLUSION

The application of fixed-point rotational reduction of the spine has shown remarkable efficacy in alleviating pain among patients diagnosed with lumbar facet joint disorder. It also facilitates restoration of lumbar mobility, functional improvement, balance enhancement, and patient rehabilitation. These findings highlight its clinical relevance and potential for therapeutic intervention.

Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models

PURPOSE

Application of a deep learning model visualization plugin for rapid and accurate automatic quantification and classification of lumbar disc herniation (LDH) types on axial T2-weighted MRIs.

METHODS

Retrospective analysis of 2500 patients, with the training set comprising data from 2120 patients (25,554 images), an internal test set covering data from 80 patients (784 images), and an external test set including data from 300 patients (3285 images). To enhance implementation, this study categorized normal and bulging discs as a grade without significant abnormalities, defining the region and severity grades of LDH based on the relationship between the disc and the spinal canal. The automated detection training and validation process employed the YOLOv8 object detection model for target area localization, the YOLOv8-seg segmentation model for disc recognition, and the YOLOv8-pose keypoint detection model for positioning. Finally, the stability of the detection results was verified using metrics such as Intersection over Union (IoU), mean error (ME), precision (P), F1 score (F1), Kappa coefficient (kappa), and 95% confidence interval (95%CI).

RESULTS

The segmentation model achieved an mAP50:95 of 98.12% and an IoU of 98.36% in the training set, while the keypoint detection model achieved an mAP50:95 of 93.58% with a mean error (ME) of 0.208 mm. For the internal and external test sets, the segmentation model's IoU was 97.58 and 97.49%, respectively, while the keypoint model's ME was 0.219 mm and 0.221 mm, respectively. In the quantification validation of the extent of LDH, P, F1, and kappa were measured. For LDH classification (18 categories), the internal and external test sets showed P = 81.21% and 74.50%, F1 = 81.26% and 74.42%, and kappa = 0.75 (95%CI 0.68, 0.82, p = 0.00) and 0.69 (95%CI 0.65, 0.73, p = 0.00), respectively. For the severity grades of LDH (four categories), the internal and external test sets showed P = 92.51% and 90.07%, F1 = 92.36% and 89.66%, and kappa = 0.88 (95%CI 0.80, 0.96, p = 0.00) and 0.85 (95%CI 0.81, 0.89, p = 0.00), respectively. For the regions of LDH (eight categories), the internal and external test sets showed P = 83.34% and 77.87%, F1 = 83.85% and 78.21%, and kappa = 0.77 (95%CI 0.70, 0.85, p = 0.00) and 0.71 (95%CI 0.67, 0.75, p = 0.00), respectively.

CONCLUSION

The automated aided diagnostic model achieved high performance in detecting and classifying LDH and demonstrated substantial consistency with expert classification.

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. This is particularly valuable for understanding the intricate dynamics of spinal surgery and its multifaceted impacts on patient prognosis. This review aims to delineate the role of artificial intelligence in spinal surgery. A search of the PubMed database from 1992 to 2023 was conducted using relevant English publications related to the application of artificial intelligence in spinal surgery. The search strategy involved a combination of the following keywords: "Artificial neural network," "deep learning," "artificial intelligence," "spinal," "musculoskeletal," "lumbar," "vertebra," "disc," "cervical," "cord," "stenosis," "procedure," "operation," "surgery," "preoperative," "postoperative," and "operative." A total of 1,182 articles were retrieved. After a careful evaluation of abstracts, 90 articles were found to meet the inclusion criteria for this review. Our review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events. By utilizing these technologies, surgical outcomes can be improved, ultimately enhancing the quality of life for patients.