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An introduction and overview of machine learning in neurosurgical care

Acta Neurochirurgica volume 160, pages 29–38 (2018)Cite this article

Abstract

Background

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML’s potential to assist and improve neurosurgical care.

Method

A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.

Results

Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson’s disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.

Conclusions

ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

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Authors and Affiliations

  1. Department of Neurosurgery, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands

    Joeky T. Senders & Marike L. Broekman

  2. Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA

    Joeky T. Senders, Mark M. Zaki, Aditya V. Karhade, Bliss Chang, William B. Gormley, Marike L. Broekman, Timothy R. Smith & Omar Arnaout

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Correspondence to Omar Arnaout.

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Conflict of interest

J.T.S., M.M.Z., O.A., A.V.K., B.C., M.L.B., T.R.S. have nothing to disclose. W.B.G.: Codman, Coviden Proctor, Consultant.

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Joeky T. Senders and Mark M. Zaki shared first author.

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Senders, J.T., Zaki, M.M., Karhade, A.V. et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir 160, 29–38 (2018). https://doi.org/10.1007/s00701-017-3385-8

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Keywords

  • Artificial intelligence
  • Machine learning
  • Neurosurgery