{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:50:07Z","timestamp":1788497407712,"version":"build-2803163510"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and\/or ID cases including 100 UAN lesions (average sizes of 2.56\u2009\u00b1\u20091.45\u00a0mm) and 40 ID lesions (average sizes of 1.75\u2009\u00b1\u20090.41\u00a0mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>EIRL_BA showed a total of 203 candidates (an average of 1.73\/case) in UAN and\/or ID cases and 98 candidates (an average of 0.80\/case) in normal controls. For diagnosing either UAN\/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates\u2019 first and second ranks in UAN (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>EIRL_BA is applicable for detecting small UAN, and the CNN\u2019s final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11604-022-01341-7","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T12:02:32Z","timestamp":1664452952000},"page":"131-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN\u2019s final layer for distinguishing between aneurysm and infundibular dilatation"],"prefix":"10.1007","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9985-3331","authenticated-orcid":false,"given":"Makiko","family":"Ishihara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masato","family":"Shiiba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirotaka","family":"Maruno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Kato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Ohmoto-Sekine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Choppin","family":"Antoine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuyoshi","family":"Ouchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"1341_CR1","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.wneu.2019.04.211","volume":"127","author":"A Morita","year":"2019","unstructured":"Morita A. 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