Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.

ABSTRACT:

Purpose

Vestibular schwannomas (VSs) represent the most common cerebellopontine angle tumors, posing a challenge in preserving facial nerve (FN) function during surgery. We employed the Extreme Gradient Boosting machine learning classifier to predict long-term FN outcomes (classified as House-Brackmann grades 1-2 for good outcomes and 3-6 for bad outcomes) after VS surgery.

Methods

In a retrospective analysis of 256 patients, comprehensive pre-, intra-, and post-operative factors were examined. We applied the machine learning (ML) classifier Extreme Gradient Boosting (XGBoost) for the following binary classification: long-term good and bad FN outcome after VS surgery To enhance the interpretability of our model, we utilized an explainable artificial intelligence approach.

SUBMITTER: Przepiorka L 

PROVIDER: S-EPMC11685252 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature