Machine learning in neurosurgery: a global survey
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ORIGINAL ARTICLE - NEUROSURGERY GENERAL
Machine learning in neurosurgery: a global survey Victor E. Staartjes 1,2,3 & Vittorio Stumpo 1,4 & Julius M. Kernbach 5 & Anita M. Klukowska 3,6 & Pravesh S. Gadjradj 7,8 & Marc L. Schröder 3 & Anand Veeravagu 9 & Martin N. Stienen 1 & Christiaan H. B. van Niftrik 1 & Carlo Serra 1 & Luca Regli 1 Received: 16 July 2020 / Accepted: 10 August 2020 # The Author(s) 2020
Abstract Background Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations. Keywords Machine learning . Artificial intelligence . Technology . Neurosurgery . Global . Worldwide survey
Introduction Recent years have witnessed the rise of machine learning applications in the scientific literature, both in basic science and clinical medicine [18, 26]. Neurosurgical practice has always relied on the individual experience of surgeons to carefully
balance surgical indications, operative risk and expected outcome [30]. The advent of evidence-based medicine has framed the surgical decision-making process into guidelines based on the results of high-quality data, and of randomized controlled clinical trials—not devoid of several flaws in design themselves [19]. This approach, despite remaining the
This article is part of the Topical Collection on Neurosurgery general * Victor E. Staartjes [email protected]
4
Università Cattolica del Sacro Cuore, Rome, Italy
5
Department of Neurosurgery, RWTH Aach
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