{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:29:16Z","timestamp":1793161756084,"version":"4.5.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"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. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Machine learning to enable precise, non-invasive detection of cerebral amyloid-beta (A\u03b2) pathology by integrating cognitive assessments, plasma biomarkers, and structural neuroimaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an explainable multimodal machine-learning framework to predict amyloid PET visual read status using plasma biomarkers, cognitive assessments, APOE genotype, demographic variables, and structural MRI measurements. The development cohort consisted of 170 participants from the 1Florida Alzheimer\u2019s Disease Research Center (ADRC) and 129 participants from the ADNI4 cohort. Machine-learning pipelines were evaluated using combinations of five classifiers and multiple feature-selection approaches with Bayesian hyperparameter optimization and stratified five-fold cross-validation. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ensemble methods consistently outperformed linear and distance-based classifiers. The optimal pipeline, XGBoost with mutual-information feature selection, achieved a mean AUC of 0.891 \u00b1 0.048 and a recall of 0.84. SHAP analyses identified the plasma p-tau217\/A\u03b242 ratio as the most influential predictor, followed by plasma p-tau217, p-tau181, MMSE, and APOE \u03b54 status. Structural MRI variables provided complementary predictive information. ADNI4 evaluation yielded a mean AUC of 0.545, while training on ADRC and testing on ADNI4 achieved an AUC of 0.636 and an overall accuracy of 63%. Importantly, plasma p-tau217\/A\u03b242 and p-tau217 remained the dominant predictors across cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multimodal machine learning can predict amyloid PET status while providing biologically interpretable explanations. Plasma tau and amyloid-related biomarkers carried the strongest predictive signal, while cognition, APOE genotype, and MRI refined classification decisions. External validation highlighted the challenges of cohort heterogeneity and domain shift but demonstrated preservation of biologically meaningful biomarker relationships.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1905974","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T05:25:35Z","timestamp":1786944335000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning for the prediction of amyloid PET positivity using plasma biomarkers, cognition, APOE genotype, and structural imaging"],"prefix":"10.3389","volume":"20","author":[{"given":"Thony","family":"Yan Liang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueting","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micheal","family":"Adeyosoye","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luana","family":"Okino Sawada","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]},{"name":"Knight Foundation School of Computing and Info Sciences, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mercedes","family":"Cabrerizo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosie E. Curiel","family":"Cid","sequence":"additional","affiliation":[{"name":"1Florida Alzheimer\u2019s Disease Research Center","place":["Gainesville, FL, United States"]},{"name":"Department of Psychiatry, Behavioral Sciences and Neurology, Miller School of Medicine, University of Miami","place":["Miami, FL, United States"]},{"name":"Center for Cognitive Neuroscience and Aging","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armando","family":"Barreto","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naphtali","family":"Rishe","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]},{"name":"Center for Cognitive Neuroscience and Aging","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Loewenstein","sequence":"additional","affiliation":[{"name":"Knight Foundation School of Computing and Info Sciences, Florida International University","place":["Miami, FL, United States"]},{"name":"1Florida Alzheimer\u2019s Disease Research Center","place":["Gainesville, FL, United States"]},{"name":"Mount Sinai Medical Center, Wien Center for Alzheimer\u2019s Disease and Memory Disorders","place":["Miami Beach, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranjan","family":"Duara","sequence":"additional","affiliation":[{"name":"Knight Foundation School of Computing and Info Sciences, Florida International University","place":["Miami, FL, United States"]},{"name":"Department of Psychiatry, Behavioral Sciences and Neurology, Miller School of Medicine, University of Miami","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malek","family":"Adjouadi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University","place":["Miami, FL, United States"]},{"name":"Knight Foundation School of Computing and Info Sciences, Florida International University","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e70304","DOI":"10.1002\/alz.70304","article-title":"Sex differences in amyloid PET in a large, real-world sample from the imaging dementia-evidence for amyloid scanning (IDEAS) study.","volume":"21","author":"Abu Raya","year":"2025","journal-title":"Alzheimers Dement"},{"key":"B2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1001\/jamaneurol.2023.5319","article-title":"Diagnostic accuracy of a plasma phosphorylated tau 217 immunoassay for alzheimer disease pathology.","volume":"81","author":"Ashton","year":"2024","journal-title":"JAMA Neurol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e12617","DOI":"10.1002\/dad2.12617","article-title":"Plasma p-tau217 concordance with amyloid PET among ethnically diverse older adults.","volume":"16","author":"Asken","year":"2024","journal-title":"Alzheimers Dement"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1038\/s41591-024-02869-z","article-title":"Highly accurate blood test for Alzheimer\u2019s disease is similar or superior to clinical cerebrospinal fluid tests.","volume":"30","author":"Barth\u00e9lemy","year":"2024","journal-title":"Nat. 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