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

Background And Objective: Preoperative neurosurgical planning is an important step in 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 datasets and potentially generating efficient and accurate algorithms in patient care. We explored the evolving applications of ML algorithms in the preoperative planning of brain and spine surgery.

Methods: In accordance with the Arksey and O'Malley framework, a scoping review was conducted using 3 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.

Results: Our search strategy yielded 7407 articles, of which 8 studies (5 retrospective, 2 prospective, and 1 experimental) 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 networks (14.3%), logistic regression (14.3%), and random forest (14.3%), among others. Neurosurgical fields of applications included functional neurosurgery (37.5%), tumor surgery (37.5%), and spine surgery (25%). The main advantages of ML included automated processing of clinical and imaging information, selection of an individualized patient surgical approach, and data-driven support for treatment decision-making. All studies reported technical limitations, such as long processing time, algorithmic bias, limited generalizability, and the need for database updating and maintenance.

Conclusions: ML algorithms for preoperative neurosurgical planning are being developed for efficient, automated, and safe treatment decision-making. However, future studies are necessary to validate their objective performance across diverse clinical scenarios. Enhancing the robustness, transparency, and understanding of ML applications will be crucial for their successful integration into neurosurgical practice.