Computational Neurosurgery: Foundation
- PMID: 39523256
- DOI: 10.1007/978-3-031-64892-2_1
Computational Neurosurgery: Foundation
Abstract
Computational neurosurgery is a novel translational field where computational modeling and artificial intelligence are used to improve diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. By laying the foundations of the field, this chapter summarizes the main aspects and implications of artificial intelligence in the clinical neurosciences, with particular emphasis on the necessity to provide an augmented intelligence (AI+) framework to be implemented in modern and future healthcare, aimed to improve the knowledge of the brain, in all its physiopathological spectrum, and to enhance the understanding and treatment of neurological and neurosurgical diseases.
Keywords: Artificial intelligence; Augmented intelligence; Computational neurosurgery; Deep learning; Machine learning; Natural language processing; Neurosurgery.
© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.
References
-
- Kirkman MA. The role of imaging in the development of neurosurgery. J Clin Neurosci [Internet] 2015 Jan 1 [cited 2021 Jun 27];22(1):55–61. Available from: https://pubmed.ncbi.nlm.nih.gov/25150767/
-
- Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng [Internet]. 2018 Dec 17 [cited 2019 Jan 7];1. Available from: http://www.nature.com/articles/s41551-018-0324-9
-
- Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med [Internet]. 2018 Sep 13 [cited 2019 Jan 7];24(9):1337–41. Available from: http://www.nature.com/articles/s41591-018-0147-y
-
- Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med [Internet]. 2019 Jan 7 [cited 2019 Jan 15];25(1):44–56. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30617339 , 25, 44.
-
- Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, et al. Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging. Clin Cancer Res [Internet]. 2018 Mar 1 [cited 2019 Jan 20];24(5):1073–81. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29167275 , 24, 1073.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources