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Neural networks for estimation of facial palsy after vestibular schwannoma surgery

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...

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Detalles Bibliográficos
Autores principales: Rampp, Stefan, Holze, Magdalena, Scheller, Christian, Strauss, Christian, Prell, Julian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10068649/
https://www.ncbi.nlm.nih.gov/pubmed/36333576
http://dx.doi.org/10.1007/s10877-022-00928-9
Descripción
Sumario: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 (chi(2) = 54.8), compared to using tumor size or mean traintime alone (chi(2) = 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 (chi(2) = 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.