{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:55:53Z","timestamp":1787921753186,"version":"build-2784847793"},"reference-count":41,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":71,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study evaluates the clinical value of a deep learning\u2013based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age\u2010 and sex\u2010adjusted percentile comparisons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty\u2010five patients\u201417 with Alzheimer's disease (AD), 18 with frontotemporal dementia (FTD), and 20 healthy controls\u2014underwent cranial magnetic resonance imaging scans. Two board\u2010certified neuroradiologists (BCNR), two board\u2010certified radiologists (BCR), and three radiology residents (RR) assessed the scans twice: first without AI support and then with AI assistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      AI significantly improved diagnostic accuracy for AD (area under the curve \u2212AI: 0.800, +AI: 0.926,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05), with increased correct diagnoses (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01) and reduced errors (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.03). BCR and RR showed notable performance gains (BCR:\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.04; RR:\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.02). For the diagnosis FTD, overall consensus (\n                      <jats:italic>p<\/jats:italic>\n                      &lt;\u00a00.01), BCNR (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.02), and BCR (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05) recorded significantly more correct diagnoses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AI\u2010assisted volumetry improves diagnostic performance in differentiating AD and FTD, benefiting all reader groups, including BCNR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Artificial intelligence (AI)\u2010supported brain volumetry significantly improved the diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), with notable performance gains across radiologists of varying expertise levels.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The presented AI tool is readily clinically available and reduces brain volumetry processing time from 12 to 24\u00a0hours to under 5 minutes, with full integration into picture archiving and communication systems, streamlining the workflow and facilitating real\u2010time clinical decision making.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>AI\u2010supported rapid brain volumetry has the potential to improve early diagnosis and to improve patient management.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70037","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T13:22:31Z","timestamp":1733923351000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Artificial intelligence\u2013based rapid brain volumetry substantially improves differential diagnosis in dementia"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4849-8034","authenticated-orcid":false,"given":"Jan","family":"Rudolph","sequence":"first","affiliation":[{"name":"Department of Radiology University Hospital LMU Munich  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Rueckel","sequence":"additional","affiliation":[{"name":"Department of Radiology University Hospital LMU Munich  Munich Germany"},{"name":"Department of Neuroradiology University Hospital LMU Munich  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rg","family":"D\u00f6pfert","sequence":"additional","affiliation":[{"name":"Development Department Mediaire GmbH  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen Xin","family":"Ling","sequence":"additional","affiliation":[{"name":"Development Department Mediaire GmbH  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Opalka","sequence":"additional","affiliation":[{"name":"Medical Department Mediaire GmbH  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Brem","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology University Hospital LMU Munich  Munich 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