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Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Fivefold cross-validation is conducted over the data and evaluated with the metrics <jats:italic>F<\/jats:italic>1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean <jats:italic>F<\/jats:italic>1 of up to 0.900 \u00b1 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-022-02713-0","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T06:02:27Z","timestamp":1659592947000},"page":"1591-1599","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4309-169X","authenticated-orcid":false,"given":"Niclas","family":"Bockelmann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Schetelig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denise","family":"Kesslau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Buschschl\u00fcter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Floris","family":"Ernst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo Mario","family":"Bonsanto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"2713_CR1","doi-asserted-by":"publisher","first-page":"977","DOI":"10.3171\/2015.5.JNS142087","volume":"124","author":"YM Li","year":"2016","unstructured":"Li YM, Suki D, Hess K, Sawaya R (2016) The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? 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