{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T10:06:14Z","timestamp":1771927574335,"version":"3.50.1"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To assess the reliability of semi-quantitative and AI-based quantitative brain volume evaluation (Quantib\u00ae ND) in predicting clinical diagnosis in patients with suspected neurodegenerative diseases undergoing initial 1.5\u2005T MRI. Additionally, to analyze the frequency of lobar microbleeds (MBs) at diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Two neuroradiologists (2 vs. 10 years\u2019 experience), blinded to diagnosis, independently evaluated brain atrophy on 3D-T1 images of 133 subjects using Scheltens, Koedam, and Kipps scales. Automated volumetric analysis was performed using Quantib\u00ae ND. SWI images were assessed by one neuroradiologist to classify MBs as cortical, juxtacortical, subcortical, or deep. Inter-observer agreement was measured using intraclass correlation coefficients (ICC); correlation with Quantib\u00ae ND was analyzed using Spearman's coefficient. Cohen's Kappa assessed agreement with clinical diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Good inter-observer agreement was observed for the MTA scale (ICC 0.86 right, 0.82 left) and Kipps scale (ICC 0.76), with moderate concordance for Koedam (ICC 0.66). Frontal and posterior temporal Kipps subregions had good concordance (ICC 0.77, 0.79), while anterior temporal showed poor agreement (ICC 0.59). Diagnostic accuracy was moderate across observers and Quantib\u00ae ND. Observer 1 showed 77% sensitivity, 51% specificity; observer 2 had 79% sensitivity, 62% specificity; Quantib\u00ae ND reached 56% sensitivity, 74% specificity. Patients exhibited significantly more lobar MBs than non-dementia patients (\n                      <jats:italic>\u03c7<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Semi-quantitative visual scales proved effective and sensitive for detecting brain atrophy, showing good concordance with automated volumetric data. While AI-based quantification demonstrated higher specificity, visual assessment remained more sensitive. Lobar MBs were more frequent in neurodegenerative cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fradi.2026.1667306","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:32:02Z","timestamp":1769517122000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MRI approach to the patient with suspected dementia: artificial intelligence techniques and semi-quantitative rating scales compared"],"prefix":"10.3389","volume":"6","author":[{"given":"S. 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G.","family":"Spinelli","sequence":"additional","affiliation":[{"name":"Neurophysiology Service, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]},{"name":"Vita-Salute San Raffaele University","place":["Milan, Italy"]},{"name":"Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Rugarli","sequence":"additional","affiliation":[{"name":"Center for Alzheimer\u2019s Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]},{"name":"Vita-Salute San Raffaele University","place":["Milan, Italy"]},{"name":"Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Ghirelli","sequence":"additional","affiliation":[{"name":"Center for Alzheimer\u2019s Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]},{"name":"Vita-Salute San Raffaele University","place":["Milan, Italy"]},{"name":"Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Magnani","sequence":"additional","affiliation":[{"name":"Neurology Unit, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Caso","sequence":"additional","affiliation":[{"name":"Neurology Unit, IRCCS San Raffaele Scientific Institute","place":["Milan, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"van Loon","sequence":"additional","affiliation":[{"name":"DeepHealth","place":["Boston, MA, United 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