European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
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The aim of this study was to assess the clinical efficacy of balanced halo-pelvic traction (HPT) and evaluate its contribution to the correction surgery in treating adult severe rigid spinal deformity. ⋯ HPT is effective for the treatment of severe rigid spinal deformity. Balanced HPT can dramatically improve coronal and sagittal deformity as well as spinal length before corrective surgery.
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It has been suggested that the cause of the balance disorder seen in adolescent idiopathic scoliosis (AIS) originates from the central nervous system. However, the extent of the balance problem and the dysfunction of which part of the central nervous system has not been investigated in detail. This study aimed to correlate the values obtained by balance analysis and cerebellum volume measurement in female individuals with AIS with healthy individuals. ⋯ Balance problems in patients with AIS are correlated with decreased cerebellum volume and increased trunk oscillation velocity.
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Review Meta Analysis
Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis.
Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists. ⋯ This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.
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Multicenter Study
Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.
To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease. ⋯ The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.
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To systematically investigate all published literature on spinal gout regarding location demographics, patient characteristics, treatment, and outcomes. ⋯ Published cases of spinal gout have increased over the last decades. Patient characteristics of spinal gout were similar to findings in systemic gout. Trends identified in patient characteristics and treatment outcomes may help guide patient management and improve our understanding of spinal gout.