Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions

Introduction: Machine learning algorithms have received increased attention in neurosurgical literature for improved accuracy over traditional predictive methods. In this review, the authors sought to assess current applications of machine learning for outcome prediction of neurosurgical treatment of intracranial aneurysms and identify areas for future research. Methods: A PRISMA-compliant systematic review of the PubMed, MEDLINE, and EMBASE databases was con-ducted for all studies utilizing machine learning for outcome prediction of intracranial aneurysm treatment. Patient characteristics, machine learning methods, outcomes of interest, and accuracy metrics were recorded from included studies. Results: 16 studies were ultimately included in qualitative synthesis. Studies primarily analyzed angiographic outcomes, functional outcomes, or complication prediction using clinical, radiological, or composite variables. The majority of included studies utilized supervised learning algorithms for analysis of dichotomized outcomes. Conclusions: Commonly included variables were demographics, presentation variables (including ruptured or unruptured status), and treatment used. Areas for future research include increased generalizability across in-stitutions and for smaller datasets, as well as development of front-end tools for clinical applicability of published algorithms.