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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This study aimed to investigate the neurological outcome, trends and sequelae following surgical or conservative treatment of intramedullary spinal cord cavernous malformations (ISCCMs). ⋯ Whenever feasible, symptomatic patients with ISCCM are recommended to undergo surgery within 3 months from symptom onset. Absence of multiple lesions and, most importantly, post-operative symptom improvement foresee a favourable long-term outcome. Further research is warranted to discern the role of conservative treatment in symptomatic patients.
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The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. ⋯ Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Review
Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.
As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. ⋯ Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.
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To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL). ⋯ Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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To explore risk profiles of patients scheduled for lumbar spinal fusion (LSF) and their association with short-term recovery of patient after surgery. ⋯ This study found a fit and deconditioned risk profile. The patients with a fit risk profile perceived a better quality of life, performed better in mobility, motor control, cardiopulmonary tests and showed also a significant shorter stay in the hospital and a shorter time to functional recovery. Preoperatively establishing a patient's risk profile could aid in perioperative care planning and preoperative decision-making.