{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:10:43Z","timestamp":1790143843132,"version":"4.0.1"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"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. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) lacks widely accessible, noninvasive approaches for the evaluation of AD remain limited. We developed a multimodal machine-learning model that integrates retinal microvascular features derived from optical coherence tomography angiography (OCTA) with clinical variables to classify AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross-sectional case\u2013control study, 160 participants (81 healthy controls and 79 patients with AD) underwent macular OCTA imaging. Twenty-seven superficial capillary plexus features, including vessel density (VD), perfusion density (PD), and foveal avascular zone (FAZ) metrics, were analyzed. Logistic regression, support vector machine, random forest, and XGBoost classifiers were evaluated using stratified five-fold cross-validation. Feature selection was performed with nested cross-validated recursive feature elimination, and model interpretability was assessed with Shapley additive explanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Patients with AD showed reduced VD and PD across multiple macular subfields. The multimodal random forest model achieved the highest area under the receiver operating characteristic curve (AUC\u202f=\u202f0.853), whereas the multimodal XGBoost model achieved the highest sensitivity (0.800) with an AUC of 0.839. SHAP analysis identified whole-macula VD, nasal inner-ring VD, FAZ circularity, and inferior inner-ring PD as influential predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings provide proof-of-concept evidence that OCTA-derived retinal microvascular features, particularly when combined with selected clinical variables, may help distinguish clinically diagnosed AD from HC in this selected case\u2013control cohort. Their utility for population screening or clinical diagnosis remains unestablished and requires prospective external validation in consecutive, clinically representative cohorts including mild cognitive impairment (MCI), other dementias, and relevant neurological and ocular differential diagnoses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1911592","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:50:29Z","timestamp":1790142629000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal machine-learning diagnostic model for Alzheimer\u2019s disease: integrating OCTA-derived retinal microvascular features and clinical variables"],"prefix":"10.3389","volume":"18","author":[{"given":"Qifeng","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]},{"name":"School of Public Health, Zunyi Medical University","place":["Zunyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongping","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Neurology, Shanghai Putuo People's Hospital, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Clinical Research Center, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengxiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]},{"name":"School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlong","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]},{"name":"Clinical Research Center, Tongji Hospital, School of Medicine, Tongji University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.1111\/ene.14871","article-title":"Novel noninvasive biomarkers of prodromal Alzheimer disease: the role of optical coherence tomography and optical coherence tomography-angiography","volume":"28","author":"Biscetti","year":"2021","journal-title":"Eur. 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