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An expert neuroradiologist identified acute ischemic, hemorrhagic, and space-occupying lesions as references. Two blinded radiology residents rated MRI image quality and motion artifacts. The diagnostic accuracy of a CE-marked deep learning tool was compared to that of radiology reports. Multivariate analysis examined associations between patient characteristics and motion artifacts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      775 patients (68 years \u00b1 16, 420 female) were included. Acute ischemic, hemorrhagic, and space-occupying lesions were found in 216 (27.9%), 12 (1.5%), and 20 (2.6%). Motion artifacts were present in 57 (7.4%). Increasing age (OR per decade, 1.60; 95% CI: 1.26, 2.09;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and limb motor symptoms (OR, 2.36; 95% CI: 1.32, 4.20;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003) were independently associated with motion artifacts in multivariate analysis. Motion artifacts significantly reduced the accuracy of detecting hemorrhage. This reduction was greater for the AI tool (from 88 to 67%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) than for radiology reports (from 100 to 93%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Ischemic and space-occupying lesion detection was not significantly affected.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Motion artifacts are common in suspected stroke patients, particularly in the elderly and patients with motor symptoms, reducing accuracy for hemorrhage detection by both AI and radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Motion artifacts reduce the quality of MRI scans, but it is unclear which factors are associated with them and how they impact diagnostic accuracy.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Motion artifacts occurred in 7% of suspected stroke MRI scans, associated with higher patient age and motor symptoms, lowering hemorrhage detection by AI and radiologists.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Motion artifacts in stroke brain MRIs significantly reduce the diagnostic accuracy of human and AI detection of intracranial hemorrhages. Elderly patients and those with motor symptoms may benefit from a greater focus on motion artifact prevention and reduction.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11807-7","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T15:29:43Z","timestamp":1752593383000},"page":"265-277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Motion artifacts and image quality in stroke MRI: associated factors and impact on AI and human diagnostic accuracy"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5994-3132","authenticated-orcid":false,"given":"Christian Hedeager","family":"Krag","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix Christoph","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen Lind","family":"Gandrup","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael Brun","family":"Andersen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob M\u00f8llenbach","family":"M\u00f8ller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie Louise","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anita","family":"Rud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Krabbe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lamaa","family":"Al-Farra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mads","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Kruuse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikael Ploug","family":"Boesen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"11807_CR1","doi-asserted-by":"publisher","first-page":"232","DOI":"10.3389\/fneur.2017.00232","volume":"8","author":"I Havsteen","year":"2017","unstructured":"Havsteen I, Ohlhues A, Madsen KH et al (2017) Are movement artifacts in magnetic resonance imaging a real problem?\u2014A narrative review. 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Financial support from Innovation Fund Denmark. Affiliated with Radiology AI Testcenter (RAIT.dk). F.C.M.: None related to this study. Prior consultancy work for Cerebriu. Prior consultancy work for Siemens Healthineers and institutional research agreement with Siemens Healthineers. Affiliated with Radiology AI Testcenter (RAIT.dk). K.L.G.: None relevant. M.B.A.: None related to this study. Prior speaker agreements with Siemens Healthineers, Philips Healthcare, GE Healthcare, Boehringer Ingelheim, and Roche. Institutional research agreement with Siemens Healthineers. Affiliated with Radiology AI Testcenter (RAIT.dk). J.M.M.: None relevant. M.L.L.: None relevant. A.R.: None relevant. S.K.: None relevant. L.A.: None relevant. M.N.: Shareholder in Cerebriu A\/S. C.K.: Local PI on studies from Bayer A\/S, and Coloplast, speakers fee from Lundbeck A\/S all unrelated to the current trial. Research fund from Innovation Fund Denmark for a collaborative project with Cerebriu A\/S. M.P.B.: None related to this study. Unrelated to this study, M.B. has received speaker and consultancy fees from Novartis, Esaote, and Image Analysis Group. Financial support from EUROSTAR, EIC-grant, Innovation Fund Denmark, Agency for Digital Government and M.B. is an advisory board member of Chondrometrics and Image Analysis group as shareholder of Image Analysis Group and Radiobotics. Affiliated with Radiology AI Testcenter (RAIT.dk).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"F.C.M. and M.P.B. kindly provided statistical advice for this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The patients without severe artifacts in this cohort (\n                      n\n                      \u2009=\u2009767) were previously evaluated with a deep learning tool in an external validation study (\n                      \n                      ).","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}