Prediction of facial nerve outcomes after surgery for vestibular schwannoma using machine learning-based models: a systematic review and meta-analysis

Abstract

Postoperative facial nerve (FN) dysfunction is associated with a significant impact on the quality of life of patients and can result in psychological stress and disorders such as depression and social isolation. Preoperative prediction of FN outcomes can play a critical role in vestibular schwannomas (VSs) patient care. Several studies have developed machine learning (ML)-based models in predicting FN outcomes following resection of VS. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of ML-based models in predicting FN outcomes following resection in the setting of VS. On December 12, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated the performance outcomes of the ML-based predictive models were included. The pooled sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated through the R program. Five studies with 807 individuals with VS, encompassing 35 models, were included. The meta-analysis showed a pooled sensitivity of 82 (95CI: 76-87), specificity of 79 (95CI: 74-84), and DOR of 12.94 (95CI: 8.65-19.34) with an AUC of 0.841. The meta-analysis of the best performance model demonstrated a pooled sensitivity of 91 (95CI: 80-96), specificity of 87 (95CI: 82-91), and DOR of 46.84 (95CI: 19.8-110.8). Additionally, the analysis demonstrated an AUC of 0.92, a sensitivity of 0.884, and a false positive rate of 0.136 for the best performance models. ML-based models possess promising diagnostic accuracy in predicting FN outcomes following resection.