Machine Learning Algorithms for Neurosurgical Preoperative Planning: A Comprehensive Scoping Review

Preoperative neurosurgical planning is a keen step to avoiding surgical complications, reducing morbidity, and improving patient safety. The incursion of machine learning (ML) in this domain has recently gained attention, given the notable advantages in processing large data sets and potentially generating efficient and accurate algorithms in patient care. Herein, we evaluated the evolving applications of ML algorithms in the preoperative planning of brain and spine surgery.BACKGROUND AND OBJECTIVESPreoperative neurosurgical planning is a keen step to avoiding surgical complications, reducing morbidity, and improving patient safety. The incursion of machine learning (ML) in this domain has recently gained attention, given the notable advantages in processing large data sets and potentially generating efficient and accurate algorithms in patient care. Herein, we evaluated the evolving applications of ML algorithms in the preoperative planning of brain and spine surgery.In accordance with the Arksey and O'Malley framework, a scoping review was conducted using three databases (Pubmed, Embase, and Web of Science). Articles that described the use of ML for preoperative planning in brain and spine surgery were included. Relevant data were collected regarding the neurosurgical field of application, patient baseline features, disease description, type of ML technology, study's aim, preoperative ML algorithm description, and advantages and limitations of ML algorithms.METHODSIn accordance with the Arksey and O'Malley framework, a scoping review was conducted using three databases (Pubmed, Embase, and Web of Science). Articles that described the use of ML for preoperative planning in brain and spine surgery were included. Relevant data were collected regarding the neurosurgical field of application, patient baseline features, disease description, type of ML technology, study's aim, preoperative ML algorithm description, and advantages and limitations of ML algorithms.Our search strategy yielded 7,407 articles, of which 8 studies (5 retrospective, 2 prospective, and 1 experimental study) satisfied the inclusion criteria. Clinical information from 518 patients (62.7% female; mean age: 44.8 years) was used for generating ML algorithms, including convolutional neural network (14.3%), logistic regression (14.3%), random forest (14.3%), and other algorithms. Neurosurgical fields of applications included functional neurosurgery (37.5%), tumor surgery (37.5%), and spine sur