{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:20:39Z","timestamp":1776129639536,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Cerebrospinal fluid erythrocyte load (CTRED) reflects occult red-blood-cell ingress into brain\/CSF and consequent heme\u2013iron exposure, a toxic pathway relevant to Alzheimer\u2019s disease (AD). We aimed to develop explainable machine learning (ML) models that classify high vs. low CTRED from routine, largely non-invasive inputs, and to position a blood-first workflow leveraging contemporary plasma amyloid\u2013tau biomarkers. Methods: Twenty-six ADNI participants were analyzed. Inputs were age, sex, mean arterial pressure (MAPres), amyloid (A\u03b242), total tau, phosphorylated tau, and hippocampal atrophy rate (APC) derived from longitudinal MRI. APC was computed from normalized hippocampal volumes. CTRED was binarized at the median (0 vs. &gt;0). Data were split into train (n = 20) and held-out test (n = 6). Five classifiers (linear SVM, ridge, logistic regression, random forests, and MLP) were trained in leakage-safe pipelines with stratified five-fold cross-validation. To provide a comprehensive assessment, we presented the contribution AUC, thresholded performance metrics, summarized model performance, and the permutation feature importance (PFI). Results: On the test set, SVM, ridge, logistic regression, and random forests achieved AUC = 1.00, while the MLP achieved AUC = 0.833. Across models, PFI consistently prioritized p-tau\/tau, A\u03b242, and MAPres; age, sex, and APC contributed secondarily. The attribution profile aligns with mechanisms linking BBB dysfunction and amyloid-related microvascular fragility with tissue vulnerability to heme\u2013iron. Conclusions: In this proof-of-concept study, explainable ML predicted CTRED from routine variables with biologically coherent drivers. Although ADNI measurements were CSF-based and the sample was small, the framework is non-invasive by adding plasma p-tau217\/A\u03b21\u201342 for amyloid, tau inputs, and integrating demographics, hemodynamic context, and MRI. External, plasma-based validation in larger cohorts is warranted, alongside extension to MCI and multimodal correlation (QSM, DCE-MRI) to establish clinically actionable CTRED thresholds.<\/jats:p>","DOI":"10.3390\/jcm14207360","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:29:17Z","timestamp":1760711357000},"page":"7360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["From Microbleeds to Iron: AI Prediction of Cerebrospinal Fluid Erythrocyte Load in Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6857-3927","authenticated-orcid":false,"given":"Rafail C.","family":"Christodoulou","sequence":"first","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0002-5513","authenticated-orcid":false,"given":"Georgios","family":"Vamvouras","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Technical University of Athens, 15772 Zografou, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Daniela","family":"Sarquis","sequence":"additional","affiliation":[{"name":"Department of Medicine, Universidad de Carabobo, Valencia 2001, Venezuela"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileia","family":"Petrou","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6102-3344","authenticated-orcid":false,"given":"Platon S.","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"2nd Department of Orthopaedic Surgery and Traumatology, Aghia Sophia Pediatric General Hospital, Thivon 3 Street, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ludwing","family":"Rivera","sequence":"additional","affiliation":[{"name":"Department of Medicine, American University of Antigua, Jabberwock Road, Osbourn 999152, Antigua and Barbuda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Celimar","family":"Morales Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Medicine, American University of Antigua, Jabberwock Road, Osbourn 999152, Antigua and Barbuda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gipsany","family":"Rivera","sequence":"additional","affiliation":[{"name":"Department of Medicine, American University of Antigua, Jabberwock Road, Osbourn 999152, Antigua and Barbuda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8863-3635","authenticated-orcid":false,"given":"Sokratis G.","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, Eginition Hospital, 15772 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evros","family":"Vassiliou","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Kean University, Union, NJ 07083, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","unstructured":"(2025, September 03). 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