{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T13:05:05Z","timestamp":1779541505022,"version":"3.53.1"},"reference-count":50,"publisher":"IOP Publishing","issue":"10","license":[{"start":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T00:00:00Z","timestamp":1540339200000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T00:00:00Z","timestamp":1540339200000},"content-version":"tdm","delay-in-days":23,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Sensocure AS"},{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"crossref","award":["Integrisc project number 219819"],"award-info":[{"award-number":["Integrisc project number 219819"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2018,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30%\u201393%) of patients with acute mesenteric ischemia (AMI) since the 1980s. As the accuracy from the first laparotomy alone is 50%, the gold standard is a second-look laparotomy, increasing the accuracy to 87%\u201389%. This study investigates the use of machine learning to classify intestinal viability and histological grading in pig jejunum, based on multivariate time-series of bioimpedance sensor data.\n                    <jats:italic>Approach<\/jats:italic>\n                    : We have previously used a bioimpedance sensor system to acquire electrical parameters from perfused, ischemic and reperfused pig jejunum (7\u2009\u2009+\u2009\u200915 pigs) over 1\u201316\u2009h of ischemia and 1\u20138\u2009h of reperfusion following selected durations of ischemia. In this study we compare the accuracy of using end-point bioimpedance measurements with a feedforward neural network (FNN), versus the accuracy when using a recurrent neural network with long short-term memory units (LSTM-RNN) with bioimpedance data history over different periods of time.\n                    <jats:italic>Main results<\/jats:italic>\n                    : Accuracies in the range of what has been reported clinically can be achieved using FNN\u2019s on a single bioimpedance measurement, and higher accuracies can be achieved when employing LSTM-RNN on a sequence of data history.\n                    <jats:italic>Significance<\/jats:italic>\n                    : Intraoperative bioimpedance measurements on intestine of suspect viability combined with machine learning can increase the accuracy of intraoperative assessment of intestinal viability. Increased accuracy in intraoperative assessment of intestinal viability has the potential to reduce the high mortality and morbidity rate of the patients.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aae0ea","type":"journal-article","created":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T13:30:53Z","timestamp":1536759053000},"page":"105011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning for intraoperative prediction of viability in ischemic small intestine"],"prefix":"10.1088","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2224-9355","authenticated-orcid":false,"given":"Runar J","family":"Strand-Amundsen","sequence":"first","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"},{"name":"University of Oslo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Tronstad","sequence":"additional","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik M","family":"Reims","sequence":"additional","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finn P","family":"Reinholt","sequence":"additional","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan O","family":"H\u00f8getveit","sequence":"additional","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"},{"name":"University of Oslo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tor I","family":"T\u00f8nnessen","sequence":"additional","affiliation":[{"name":"Oslo University Hospital\u2014Rikshospitalet"},{"name":"University of Oslo\u2014Rikshospitalet"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2018,10,24]]},"reference":[{"key":"pmeaaae0eabib001","article-title":"Sound event detection in multichannel audio using spatial and harmonic features","author":"Adavanne","year":"2016","type":"conference-proceedings"},{"key":"pmeaaae0eabib0001a","article-title":"An Internet resource for the calculation of the dielectric properties of body tissues in the frequency range 10 Hz\u2013100 GHz","author":"Andreuccetti","year":"1997","type":"web-resource"},{"key":"pmeaaae0eabib002","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/S0002-9610(00)00444-X","type":"journal-article","article-title":"Assessment of intestinal viability using a non-contact laser tissue blood flowmeter","volume":"180","author":"Ando","year":"2000","journal-title":"Am. 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