In Journal of clinical monitoring and computing
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.
Rampp Stefan, Holze Magdalena, Scheller Christian, Strauss Christian, Prell Julian
2022-Nov-04
Facial nerve, Intraoperative monitoring, Machine learning, Vestibular schwannoma
