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Review
. 2021 Oct 7;13(19):5010.
doi: 10.3390/cancers13195010.

Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

Simon Williams  1   2 , Hugo Layard Horsfall  1   2 , Jonathan P Funnell  1   2 , John G Hanrahan  1   2 , Danyal Z Khan  1   2 , William Muirhead  1   2 , Danail Stoyanov  2 , Hani J Marcus  1   2
Affiliations
Review

Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

Simon Williams et al. Cancers (Basel). .

Abstract

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

Keywords: AI; artificial intelligence; brain tumour; deep learning; machine learning; neurosurgery; oncology; surgery.

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Conflict of interest statement

D/S/ is an employee of Digital Surgery, Medtronic, which is developing products related to the research described in this paper. No funding was applied for this study.

Figures

Figure 1
Figure 1
Artificial intelligence and five key subdomains. Each subdomain of AI has numerous potential clinical applications for brain tumour surgery patients. Schematic derived and modified from Panesar et al. [6] and Hashimoto et al. [9]. Numerous other subfields of AI exist, and this schematic is not exhaustive. Please add copyright if necessary.
Figure 2
Figure 2
Potential clinical impacts of AI in the neurosurgical management of brain tumours, in the pre-operative, intra-operative, and post-operative phase.
Figure 3
Figure 3
Visual representation of an artificial neural network, demonstrating the “black box” ethical problem. Numerous data inputs are processed among many hidden layers of computational units, ultimately resulting in an output. For example, data inputs may be tumour grade, location, and patient demographics. After data processing, outputs may be survival prediction or response to certain therapeutics. Inability to understand how outputs are generated due to complexity of hidden layers is referred to as the black box problem, and raises concerns regarding trust in deep learning predictive models.
See this image and copyright information in PMC

References

    1. Marcus H.J., Hughes-Hallett A., Kwasnicki R.M., Darzi A., Yang G.-Z., Nandi D. Technological innovation in neurosurgery: A quantitative study. J. Neurosurg. 2015;123:174–181. doi: 10.3171/2014.12.JNS141422. - DOI - PMC - PubMed
    1. Senders J.T., Staples P.C., Karhade A.V., Zaki M.M., Gormley W.B., Broekman M.L., Smith T.R., Arnaout O. Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurg. 2018;109:476–486. doi: 10.1016/j.wneu.2017.09.149. - DOI - PubMed
    1. Senders J.T., Zaki M.M., Karhade A.V., Chang B., Gormley W.B., Broekman M.L., Smith T.R., Arnaout O. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir. 2017;160:29–38. doi: 10.1007/s00701-017-3385-8. - DOI - PubMed
    1. Jordan M.I., Mitchell T.M. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255–260. doi: 10.1126/science.aaa8415. - DOI - PubMed
    1. Obermeyer Z., Emanuel E.J. Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med. 2016;375:1216–1219. doi: 10.1056/NEJMp1606181. - DOI - PMC - PubMed

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