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 identification of gene mutations in the modern medical workup of metastatic spine tumors has become more common but has not been highly utilized in surgical planning. Potential utility of these genetic markers as surrogates for cancer behavior in current prognosis scoring systems and overall survival (OS) remains underexplored in existing literature. This study seeks to investigate the association of frequently identified tumor markers, EGFR, ALK, and PD-L1, in metastatic non-small cell lung cancer (NSCLC) to the spine with Tokuhashi prognosis scoring and OS. ⋯ ALK and PD-L1 were significantly associated with Tokuhashi score while EGFR was not. Tumor markers alone were not predictive of OS. These findings indicate that genetic markers found in NSCLC metastases to the spine may demonstrate prognostic value. Therefore, employing standard tumor markers could enhance the identification of appropriate surgical candidates, although they demonstrate limited effectiveness in predicting overall survival.
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Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery. ⋯ Federated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care.
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Comparative Study
ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.
Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. ⋯ This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.
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Comparative Study
Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.
To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery. ⋯ Deep-learning 3D MRI facilitates equivalent virtual pedicle screw placements and geometric assessments for most lumbar vertebrae, with the exception of vertebral body length at L1, L2, and L4, compared to CT for pre-operative planning in patients considered for robotic-navigated spine surgery.