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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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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The number of articles retracted by peer-reviewed journals has increased in recent years. This study systematically reviews retracted publications in the spine surgery literature. ⋯ The retraction of publications has increased in recent years in spine surgery. Researchers consulting this body of literature should remain vigilant. Institutions and journals should collaborate to increase publication transparency and scientific integrity.
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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.