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These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)\u2013based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion\u2013based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44\u20130.63), precision 0.69 (0.60\u20130.76), and S\u00f8rensen\u2013Dice coefficient 0.61 (0.52\u20130.67). Stroke\/nonstroke differentiation accuracy 0.88 (0.81\u20130.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported <jats:italic>T<\/jats:italic><jats:sub>max<\/jats:sub>\u2009&gt;\u200910\u00a0s volumes (Pearson\u2019s <jats:italic>r<\/jats:italic>\u2009=\u20090.76 (0.58\u20130.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.<\/jats:p>","DOI":"10.1007\/s10278-022-00611-0","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:02:16Z","timestamp":1645736536000},"page":"551-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3777-5283","authenticated-orcid":false,"given":"Teemu","family":"M\u00e4kel\u00e4","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1375-7806","authenticated-orcid":false,"given":"Olli","family":"\u00d6man","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6084-9100","authenticated-orcid":false,"given":"Lasse","family":"Hokkinen","sequence":"additional","affiliation":[]},{"given":"Ulla","family":"Wilppu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8797-6094","authenticated-orcid":false,"given":"Eero","family":"Salli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8085-322X","authenticated-orcid":false,"given":"Sauli","family":"Savolainen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-3373","authenticated-orcid":false,"given":"Marko","family":"Kangasniemi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"611_CR1","doi-asserted-by":"crossref","unstructured":"Tran BX, Vu GT, Ha GH, Vuong QH, Ho MT, Vuong TT, et al.: Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. 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All the imaging procedures were performed as part of routine care.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}