Affiliation:
1. Harvard Medical School, Boston, Massachusetts;
2. Stanford University School of Medicine, Stanford, California;
3. Department of Neurosurgery, Brigham and Women’s Hospital, Boston; and
4. Department of Neurosurgery, Harvard Medical School, Boston, Massachusetts
Abstract
OBJECTIVE
Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work.
METHODS
The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment.
RESULTS
The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature.
CONCLUSIONS
ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.
Publisher
Journal of Neurosurgery Publishing Group (JNSPG)
Subject
Neurology (clinical),General Medicine,Surgery
Reference56 articles.
1. High-performance medicine: the convergence of human and artificial intelligence;Topol EJ,2019
2. An introduction and overview of machine learning in neurosurgical care;Senders JT,2018
3. Machine learning in neurosurgery: a global survey;Staartjes VE,2020
4. Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging;Chang K,2018
5. Predicting the future – big data, machine learning, and clinical medicine;Obermeyer Z,2016
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