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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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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To investigate the relationship between sagittal plane characteristics of the spinal column and conservative treatment failure in acute osteoporotic spinal fractures (OSFs). ⋯ Delayed complications requiring reconstructive surgery following OSFs are related to sagittal plane parameters of the spine such as high pelvic incidences, in addition to previously known radiographic characteristics of fractures.
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Predicting urinary retention is difficult. The aim of this study is to prospectively validate a previously developed model using machine learning techniques. ⋯ This prospective study confirms performance of the prediction model for POUR developed with retrospective data, showing great correlation. This supports the use of machine learning techniques in the prediction of postoperative complications such as urinary retention.
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An osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML). ⋯ ML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.
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To describe 1-week and 1-year prevalence of spinal pain and its consequences in relation to leisure activity, work-life, and care-seeking in people with type 1 and 2 diabetes mellitus (DM). ⋯ Spinal pain is common in people with type 1 and 2 DM, resulting in considerable consequences for work/leisure activities, sick-leave, and healthcare utilisation as compared to the general population.