Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systema
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REVIEW
Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review Quinlan D. Buchlak 1 & Nazanin Esmaili 1,2 & Jean-Christophe Leveque 3,4 & Farrokh Farrokhi 3,4 & Christine Bennett 1 & Massimo Piccardi 5 & Rajiv K. Sethi 3,4,6 Received: 22 May 2019 / Revised: 5 July 2019 / Accepted: 6 August 2019 # Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research. Keywords Artificial intelligence . Deep brain stimulation . Deep learning . Machine learning . Neurosurgery . Risk stratification . Spine surgery
Introduction * Quinlan D. Buchlak [email protected] 1
School of Medicine, The University of Notre Dame, Sydney, NSW, Australia
2
Rozetta Institute, Sydney, NSW, Australia
3
Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
4
Department of Neurosurgery, Virginia Mason Medical Center, Seattle, WA, USA
5
University of Technology Sydney, Sydney, NSW, Australia
6
Department of Health Services, University of Washington, Seattle, WA, USA
Artificial intelligence (AI) involves the use of computer systems to achieve goals by simulating cognitive capabilities [42, 79]. Machine l
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