Abstract
BackgroundRecent 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.MethodsThe 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).ResultsOut 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.ConclusionsThis 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.
Highlights
Recent years have witnessed the rise of machine learning applications in the scientific literature, both in basic science and clinical medicine [18, 26]
This study provides a first global overview of the adoption of machine learning (ML) into neurosurgical practice
Machine learning has the potential to improve diagnostic work-up and neurosurgical decision-making by shedding light on radiological interpretation, surgical outcome and complication prediction and as a consequence patients’ quality of life and surgical satisfaction
Summary
Recent years have witnessed the rise of machine learning applications in the scientific literature, both in basic science and clinical medicine [18, 26]. 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]. With the exponential growth of data in the era of big data, it is increasingly important to provide clinicians with tools for integrating this individual patient data into reliable prediction models The latter primarily aims to enhance the surgical decision-making processes and potentially improve outcomes, but predictive analytics harbour the potential to reduce unnecessary health-care costs [21, 29, 31, 34, 36, 37, 41]. 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
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