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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Networks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200954.8), compared to using tumor size or mean traintime alone (chi\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200930.6 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\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200954.8 vs. 51.3) in patients without separate intermediate nerve.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10877-022-00928-9","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T18:02:40Z","timestamp":1667584960000},"page":"575-583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Neural networks for estimation of facial palsy after vestibular schwannoma surgery"],"prefix":"10.1007","volume":"37","author":[{"given":"Stefan","family":"Rampp","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magdalena","family":"Holze","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Scheller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Strauss","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julian","family":"Prell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"928_CR1","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/s00106-017-0340-1","volume":"65","author":"J Prell","year":"2017","unstructured":"Prell J, Strauss C, Plontke SK, Rampp S. 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