Artificial intelligence innovations in neurosurgical oncology: a narrative review

作者：Clayton R. Baker, Matthew Pease, Daniel Sexton, Andrew Abumoussa, Lola B. Chambless · 发表于：Journal of Neuro-Oncology · 年份：2024 · DOI：10.1007/s11060-024-04757-5 · 被引用次数：13 · 研究领域：Glioma Diagnosis and Treatment、Artificial Intelligence in Healthcare and Education、Brain Tumor Detection and Classification

PURPOSE: Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes. METHODS: A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors. RESULTS: Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens. CONCLUSION: While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.