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The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed reality and robotic applications-is beginning to impact both open vascular as well as endovascular neurosurgery. Especially in this relatively common patient population of often very fragile patients, with potential for devastating complications and clinical outcomes and sometimes highly complex pathologies, computer assistance could prove particularly useful. In this chapter, state-of-the-art applications of machine learning toward vascular patients are elucidated: Beginning from simple clinical diagnostic, prognostic, and predictive modeling, to the interpretation of medical imaging (radiomics, segmentation, and diagnostic assistance) and synthetic imaging (image modality conversion, super-resolution, and 2D-to-3D-synthesis), up to intraoperative applications of computer vision (robotic steering, rapid intraoperative histopathology, and anatomical and surgical phase recognition), and natural language processing (enabling model training and big data, documentation, and large language models)-this chapter provides a "tour de force" of machine intelligence in the realm of neurovascular medicine.

Original publication

DOI

10.1007/978-3-031-64892-2_23

Type

Publication Date

2024

Volume

1462

Pages

383 - 395

Keywords

Artificial intelligence, Computational neuroscience, Endovascular, Machine learning, Neurosurgery, Vascular, Humans, Endovascular Procedures, Artificial Intelligence, Machine Learning, Neurosurgical Procedures, Cerebrovascular Disorders, Surgery, Computer-Assisted, Natural Language Processing