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However, OCT images are difficult to interpret, thus, real\u2010time automatic analysis could support the clinical decision\u2010making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open\u2010access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthew's correlation coefficient (MCC) of 0.79 (accuracy\u00a0=\u00a00.90) for the normal\u2010versus\u2010abnormal classification. On the open\u2010access datasets, the custom models achieved the best performance (MCC\u2009&gt;\u20090.88, accuracy\u2009&gt;\u20090.96). Results obtained for the normal\u2010versus\u2010abnormal classification suggest OCT, complemented with deep learning\u2010based analysis, as a tool for real\u2010time automatic diseased tissue identification in thyroid surgery.<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio202200227-gra-0001.png\" xlink:title=\"image\"\/><\/jats:p>","DOI":"10.1002\/jbio.202200227","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T03:58:17Z","timestamp":1665115097000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Diseased thyroid tissue classification in<scp>OCT<\/scp>images using deep learning: Towards surgical decision support"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7582-1706","authenticated-orcid":false,"given":"Iulian Emil","family":"Tampu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Link\u00f6ping University Link\u00f6ping Sweden"},{"name":"Center for Medical Image Science and Visualization Link\u00f6ping University Link\u00f6ping Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7061-7995","authenticated-orcid":false,"given":"Anders","family":"Eklund","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Link\u00f6ping University Link\u00f6ping Sweden"},{"name":"Center for Medical Image Science and Visualization Link\u00f6ping University Link\u00f6ping Sweden"},{"name":"Division of Statistics &amp; 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