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Machine learning models based on magnetic resonance imaging (MRI)\u2013 and amyloid PET\u2013derived features may serve as useful screening tools for tau pathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      Multiple machine learning models were developed to classify tau positivity in the Braak III\/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (\n                      <jats:italic>n\u00a0=<\/jats:italic>\n                      \u00a0410) data were used for model training. Open Access Series of Imaging Studies (OASIS\u20103;\n                      <jats:italic>n\u00a0=<\/jats:italic>\n                      \u00a0143) and the Standardized Centralized Alzheimer's Disease Neuroimaging (SCAN;\n                      <jats:italic>n\u00a0=<\/jats:italic>\n                      \u00a0154) data were used for external validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Logistic regression achieved the best performance with areas under the curve (AUCs) of 0.92 for both internal and external validation. Combined external validation yielded accuracy\/sensitivity\/specificity of 85%\/83%\/85%. Subjects with mild cognitive impairment and predicted tau positivity progressed to AD at a significantly faster pace (\n                      <jats:italic>p\u00a0&lt;<\/jats:italic>\n                      \u00a010\n                      <jats:sup>\u22126<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Our model demonstrates the feasibility of classifying tau burden in amyloid\u2010positive cohorts with MRI\u2010 and amyloid PET\u2013derived features and may serve as a surrogate biomarker.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.71683","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:29:46Z","timestamp":1785482986000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Classification of 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