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Journal of Neurosurgical Sciences 2021 Sep 21

DOI: 10.23736/S0390-5616.21.05483-7


language: English

Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives

Leonardo TARICIOTTI 1, 2, 3, Paolo PALMISCIANO 3, 4, Martina GIORDANO 3, 5, Giulia REMOLI 3, 6, Eleonora LACORTE 6, Giulio BERTANI 1, Marco LOCATELLI 1, 7, 8, Francesco DIMECO 9, Valerio M. CACCAVELLA 3, 5 , Francesco PRADA 9, 10

1 Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; 2 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; 3 NEVRALIS, Milan, Italy; 4 Department of Neurosurgery, Trauma, Gamma Knife Center Cannizzaro Hospital, Catania, Italy; 5 Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; 6 National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy; 7 Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; 8 Aldo Ravelli Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan, Milan, Italy; 9 Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; 10 Department of Neurological Surgery, University of Virginia Health Science Center, Charlottesville, VA, USA


BACKGROUND: Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties.
METHODS: A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models’ performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool.
RESULTS: 41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified.
CONCLUSIONS: In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.

KEY WORDS: Artificial intelligence; Machine learning; Intraoperative; Neuro-oncology; Spine; Functional neurosurgery

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