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 spinopelvic reconstruction poses significant challenges following total sacrectomy in patients with malignant or aggressive benign bone tumours encompassing the entire sacrum. In this study, we aim to assess the functional outcomes and complications of an integrated 3D-printed sacral endoprostheses featuring a self-stabilizing design, eliminating the requirement for supplemental fixation. ⋯ The utilization of 3D-printed self-stabilizing endoprosthesis proved to be a viable approach, yielding satisfactory short-term outcomes in patients undergoing total sacral reconstruction without supplemental fixation.
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To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. ⋯ The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.
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To provide lumbar spine anatomical parameters relevant to the UBE technique and explore their intraoperative application. ⋯ Referring to the drill diameter during surgery can mark the extent of laminotomy. The characteristics of vertebral plate angles at different lumbar levels can provide references for preoperative incision design.
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This study aimed to develop and validate a machine learning (ML) model to predict high-grade heterotopic ossification (HO) following Anterior cervical disc replacement (ACDR). ⋯ Through an ML approach, the model identified risk factors and predicted development of high grade HO following ACDR with good discrimination and overall performance. By addressing the shortcomings of traditional statistics and adopting a new logical approach, ML techniques can support discovery, clinical decision-making, and intraoperative techniques better.
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This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. ⋯ The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.