Purpose: Facial nerve damage in vestibular schwannoma surgery is associated with A-train patterns in free-running EMG, correlating with the degree of postoperative facial palsy. However, anatomy, preoperative functional status, tumor size and occurrence of A-trains clusters, i.e., sudden A-trains in most channels may further contribute. In the presented study, we examine neural networks to estimate postoperative facial function based on such features.
Methods: Data from 200 consecutive patients were used to train neural feed-forward networks (NN). Estimated and clinical postoperative House and Brackmann (HB) grades were compared. Different input sets were evaluated.
Results: Networks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi2 = 54.8), compared to using tumor size or mean traintime alone (chi2 = 30.6 and 31.9). Separate intermediate nerve or detection of A-train clusters did not improve performance. Removal of A-train cluster traintime improved results (chi2 = 54.8 vs. 51.3) in patients without separate intermediate nerve.
Conclusion: NN based on preoperative HB, traintime and tumor size provide good estimations of postoperative HB. The method is amenable to real-time implementation and supports integration of information from different sources. NN could enable multimodal facial nerve monitoring and improve postoperative outcomes.
Keywords: Facial nerve; Intraoperative monitoring; Machine learning; Vestibular schwannoma.
© 2022. The Author(s).