{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:23:33Z","timestamp":1793114613477,"version":"4.5.0"},"reference-count":86,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Abnormal \u03b2-amyloid (A\u03b2) accumulation in the brain is an early indicator of Alzheimer\u2019s disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer\u2019s treatments can now successfully target amyloid pathology, there is a growing interest in predicting A\u03b2 positivity (A\u03b2+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer A\u03b2\u2009+\u2009from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI\/PET data from 1,847 elderly participants (mean age: 75.1\u2009yrs. \u00b1 7.6SD; 863 females\/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer\u2019s Disease Neuroimaging Initiative. We evaluated each model\u2019s balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating A\u03b2\u2009+\u2009status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.<\/jats:p>","DOI":"10.3389\/fnins.2024.1387196","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T05:09:01Z","timestamp":1719896941000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer\u2019s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI"],"prefix":"10.3389","volume":"18","author":[{"given":"Tamoghna","family":"Chattopadhyay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saket S.","family":"Ozarkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketaki","family":"Buwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha Ann","family":"Joshy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dheeraj","family":"Komandur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayati","family":"Naik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia I.","family":"Thomopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greg","family":"Ver Steeg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Luis","family":"Ambite","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul M.","family":"Thompson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"ref1","year":"2024"},{"key":"ref2","year":""},{"key":"ref3","year":""},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.3390\/cancers13071590","article-title":"Novel transfer learning approach for medical imaging with limited labeled data","volume":"13","author":"Alzubaidi","year":"2021","journal-title":"Cancer"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.30773\/pi.2023.0052","article-title":"A case-control clinical trial on a deep learning-based classification system for diagnosis of amyloid-positive Alzheimer\u2019s disease","volume":"20","author":"Bae","year":"2023","journal-title":"Psychiatry Investig."},{"key":"ref6","author":"Bi","year":"2022"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"19024","DOI":"10.1038\/s41598-019-54204-z","article-title":"Predicting clinical decline and conversion to Alzheimer\u2019s disease or dementia using novel Elecsys Abeta(1-42), pTau and tTau CSF immunoassays","volume":"9","author":"Blennow","year":"2019","journal-title":"Sci. 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