The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review.

ABSTRACT: The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identifie

SUBMITTER: Yang DB 

PROVIDER: S-EPMC10581827 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature