{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:06:42Z","timestamp":1791522402483,"version":"4.3.4"},"reference-count":45,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102018M3C7A1054935"],"award-info":[{"award-number":["NRF\u20102018M3C7A1054935"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Euro J of Neurology"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and purpose<\/jats:title><jats:p>Alzheimer disease (AD) is the most common type of dementia. Amyloid\u2010\u03b2 (A\u03b2) positivity is the main diagnostic marker for AD. A\u03b2 positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of A\u03b2, and the accessibility of these methods is thus relatively limited compared with structural magnetic resonance imaging (sMRI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We investigated whether regions of interest (ROIs) in sMRIs can be used to predict A\u03b2 positivity for samples with normal cognition (NC), mild cognitive impairment (MCI), and dementia. We obtained 846 A\u03b2 negative (A\u03b2\u2212) and 865 A\u03b2 positive (A\u03b2+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are A\u03b2+, we built five machine learning models using ROIs and apolipoprotein E (<jats:italic>APOE<\/jats:italic>) genotypes as features. To test the performance of the machine learning models, we constructed a new cohort containing 97 A\u03b2\u2212 and 81 A\u03b2+ samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The best performing machine learning model combining ROIs and <jats:italic>APOE<\/jats:italic> had an accuracy of 0.798, indicating that it can help predict A\u03b2+. Furthermore, we searched ROIs that could aid our prediction and discovered that an average left entorhinal cortical region (L\u2010ERC) thickness is an important feature. We also noted significant differences in L\u2010ERC thickness between the A\u03b2\u2212 and A\u03b2+ samples even in the same diagnosis of NC, MCI, and dementia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our findings indicate that ROIs from sMRIs along with <jats:italic>APOE<\/jats:italic> can be used as an initial screening tool in the early diagnosis of AD.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ene.15775","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T10:10:03Z","timestamp":1678702203000},"page":"1574-1584","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Detecting amyloid\u2010\u03b2 positivity using regions of interest from structural magnetic resonance imaging"],"prefix":"10.1111","volume":"30","author":[{"given":"Jeongyoung","family":"Hwang","sequence":"first","affiliation":[{"name":"Artificial Intelligence Graduate School Gwangju 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