Book contents
- Neurosurgery
- Reviews
- Neurosurgery
- Copyright page
- Contents
- Foreword – Quo Vadis
- Preface I
- Preface II: Neurosurgery: Beyond the Cutting Edge
- Contributors
- Section I What Came Before
- Section II Ideas and Concepts in Modern Neurosurgical Innovation
- Section III Areas of Biological and Technical Advances Driving Neurosurgical Innovation
- Section IV The Surgeon’s Armamentarium
- Section V The Neurosurgeons of the Future: A Striking Metamorphosis
- Index
- References
5a - Artificial Intelligence in Neurosurgery
from Chapter 5 - The Computer as a Tool in Understanding and Managing Brain Disease
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- Neurosurgery
- Reviews
- Neurosurgery
- Copyright page
- Contents
- Foreword – Quo Vadis
- Preface I
- Preface II: Neurosurgery: Beyond the Cutting Edge
- Contributors
- Section I What Came Before
- Section II Ideas and Concepts in Modern Neurosurgical Innovation
- Section III Areas of Biological and Technical Advances Driving Neurosurgical Innovation
- Section IV The Surgeon’s Armamentarium
- Section V The Neurosurgeons of the Future: A Striking Metamorphosis
- Index
- References
Summary
In this era of rapid technological advancement, artificial intelligence (AI) has seamlessly integrated into the fabric of our lives. This widespread utilization has rekindled the discourse surrounding the current and prospective roles of AI across various domains. Amid the vast array of fields where AI applications hold promise, medicine provides a unique opportunity to harness the power and precision of AI. Before delving into the specialized domain of AI within neurosurgery, we’ll present a brief historical context of AI’s integration into the realm of medicine, and how it gradually found its way into neurosurgical practice.
The foundation of AI was laid in 1950 when Alan Turing, mathematician and computer scientist, proposed the Turing Test, aimed at determining if a machine’s ability to exhibit intelligence could be “indistinguishable” from that of a human. By the 1970s, there was active investigation of AI applications within a multitude of domains, including medicine.
Information
- Type
- Chapter
- Information
- NeurosurgeryBeyond the Cutting Edge, pp. 58 - 65Publisher: Cambridge University PressPrint publication year: 2025
References
Kwoh, YS, Hou, J, Jonckheere, EA, Hayati, S. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Transactions on Biomedical Engineering [Internet]. 1988 Feb;35(2):153–60.10.1109/10.1354CrossRefGoogle ScholarPubMed
Clark, MC, Hall, LO, Goldgof, DB, Velthuizen, R, Murtagh, FR, Silbiger, MS. Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging. 1998 Apr;17(2):187–201.10.1109/42.700731CrossRefGoogle ScholarPubMed
Prastawa, M, Bullitt, E, Ho, S, Gerig, G. A brain tumor segmentation framework based on outlier detection*1. Medical Image Analysis. 2004 Sep;8(3):275–83.10.1016/j.media.2004.06.007CrossRefGoogle Scholar
Havaei, M, Dutil, F, Pal, C, Larochelle, H, Jodoin, PM. A convolutional neural network approach to brain tumor segmentation. Lecture Notes in Computer Science. 2016 Jan; 195–208.CrossRefGoogle Scholar
Moses, DA, Metzger, SL, Liu, JR, Anumanchipalli, GK, Makin, JG, Sun, PF, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. New England Journal of Medicine. 2021 Jul;385(3):217–27.10.1056/NEJMoa2027540CrossRefGoogle Scholar
Dundar, TT, Yurtsever, I, Pehlivanoglu, MK, Yildiz, U, Eker, A, Demir, MA, et al. Machine learning-based surgical planning for neurosurgery: Artificial intelligent approaches to the cranium. Frontiers in Surgery. 2022 Apr;9.10.3389/fsurg.2022.863633CrossRefGoogle Scholar
Karabacak, M, Margetis, K. Machine learning-based prediction of short-term adverse postoperative outcomes in cervical disc arthroplasty patients. World Neurosurgery [Internet]. 2023 Jun 15 [cited 2023 Dec 10];S1878-8750(23)007969. Available from: https://pubmed.ncbi.nlm.nih.gov/37330003/Google Scholar
McAvoy, M, Prieto, PC, Kaczmarzyk, JR, Fernández, IS, McNulty, J, Smith, T, et al. Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks. Scientific Reports. 2021 Jul;11(1).10.1038/s41598-021-94733-0CrossRefGoogle ScholarPubMed
Liew, SL, Lo, B, Donnelly, MR, Zavaliangos-Petropulu, Artemis, Jeong, JN, Barisano, G, et al. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Scientific Data. 2022 Jun;9(1).10.1038/s41597-022-01401-7CrossRefGoogle ScholarPubMed
Boaro, A, Kaczmarzyk, JR, Kavouridis, VK, Harary, M, Mammi, M, Dawood, H, et al. Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice. Scientific Reports [Internet]. 2022 Sep 14 [cited 2023 Apr 10];12(1):15462.10.1038/s41598-022-19356-5CrossRefGoogle ScholarPubMed
Su, R, Zhang, D, Liu, J, Cheng, C. MSU-Net: Multi-Scale U-Net for 2D Medical image segmentation. Frontiers in Genetics. 2021 Feb; 12.10.3389/fgene.2021.639930CrossRefGoogle Scholar
Yap, MH, Pons, G, Marti, J, Ganau, S, Sentis, M, Zwiggelaar, R, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics. 2017 Jul;22(4):1218–26.Google ScholarPubMed
Candemir, S, Jaeger, S, Palaniappan, K, Musco, JP, Singh, RK, Xue, Zhiyun, et al. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging. 2013;33(2):577–90.Google ScholarPubMed
Jaeger, S, Karargyris, A, Candemir, S, Folio, L, Siegelman, J, Callaghan, F, et al. Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging. 2013;33(2):233–45.Google ScholarPubMed
Tschandl, P, Rosendahl, C, Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data [Internet]. 2018 Aug;5(1).10.1038/sdata.2018.161CrossRefGoogle ScholarPubMed
Codella, N, Rotemberg, V, Tschandl, P, Celebi, ME, Dusza, S, Gutman, D, et al. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC). arXiv:190203368 [cs] [Internet]. 2019 Mar 29.Google Scholar
Walsh, J, Othmani, A, Jain, M, Dev, S. Using U-Net Network for efficient brain tumor segmentation in MRI images. Healthcare Analytics. 2022 Aug;100098.10.1016/j.health.2022.100098CrossRefGoogle Scholar
Trebeschi, S, Drago, SG, Birkbak, NJ, Kurilova, I, Cǎlin, AM, Delli Pizzi, A, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Annals of Oncology [Internet]. 2019 Jun;30(6):998–1004.10.1093/annonc/mdz108CrossRefGoogle ScholarPubMed
Jiang, LY, Liu, XC, Nejatian, NP, Nasir-Moin, M, Wang, D, Abidin, A, et al. Health system-scale language models are all-purpose prediction engines. Nature [Internet]. 2023 Jun;1–6. Available from: https://www.nature.com/articles/s41586-023-06160-y.Google Scholar
Accessibility standard: Inaccessible, or known limited accessibility
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The PDF of this book is known to have missing or limited accessibility features.
We may be reviewing its accessibility for future improvement, but final compliance is
not yet assured and may be subject to legal exceptions. If you have any questions,
please contact
accessibility@cambridge.org.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Visual Accessibility
Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Structural and Technical Features
ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.