{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T22:32:09Z","timestamp":1789511529063,"version":"build-2803163510"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"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. Public Health"],"abstract":"<jats:p>The rising incidence of Alzheimer\u2019s disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities. Our method achieved segmentation accuracies of 79.1% on the DRIVE dataset, 68.3% on the HRF dataset. Utilizing a multimodal dataset comprising both healthy and AD subjects, ReIU extracted vascular density from fundus images, facilitating primary AD screening with a classification accuracy of 79%. These results demonstrate ReIU\u2019s substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer\u2019s disease. This study underscores the importance of integrating multi-modal data and deep learning techniques in advancing the early detection and management of Alzheimer\u2019s disease.<\/jats:p>","DOI":"10.3389\/fpubh.2024.1449798","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T06:50:02Z","timestamp":1735887002000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data"],"prefix":"10.3389","volume":"12","author":[{"given":"Hao","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yishan","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghang","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1038\/s41467-019-10212-1","article-title":"Interpretable classification of Alzheimer\u2019s disease pathologies with a convolutional neural network pipeline","volume":"10","author":"Tang","year":"2019","journal-title":"Nat Commun"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/S1474-4422(12)70200-4","article-title":"Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study","volume":"11","author":"Whitwell","year":"2012","journal-title":"Lancet Neurol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.2217\/bmm.14.42","article-title":"Advances in MRI biomarkers for the diagnosis of Alzheimer's disease","volume":"8","author":"Kehoe","year":"2014","journal-title":"Biomark Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/nrneurol.2010.4","article-title":"Cerebrospinal fluid and plasma biomarkers in Alzheimer disease","volume":"6","author":"Blennow","year":"2010","journal-title":"Nat Rev Neurol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12974-023-03003-5","article-title":"Peripheral blood amyloid-\u03b2 involved in the pathogenesis of Alzheimer\u2019s disease via impacting on peripheral innate immune cells","volume":"21","author":"Shi","year":"2024","journal-title":"J Neuroinflammation"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/RBME.2024.3376835","article-title":"Alzheimer's disease diagnosis in the preclinical stage: Normal aging or dementia","author":"Marvi","year":"2024","journal-title":"IEEE Rev Biomed Eng"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.arr.2016.01.004","article-title":"Neuroimaging biomarkers in Alzheimer's disease and other dementias","volume":"30","author":"Villemagne","year":"2016","journal-title":"Ageing Res Rev"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1136\/jnnp-2020-325347","article-title":"Retinal imaging in Alzheimer\u2019s disease","volume":"92","author":"Cheung","year":"2021","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.jalz.2018.08.005","article-title":"Understanding disease progression and improving Alzheimer's disease clinical trials: recent highlights from the Alzheimer's disease neuroimaging Initiative","volume":"15","author":"Veitch","year":"2019","journal-title":"Alzheimers Dement"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","article-title":"Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data","volume":"11","author":"Jo","year":"2019","journal-title":"Front Aging Neurosci"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.jalz.2018.07.222","article-title":"Vascular dysfunction\u2014the disregarded partner of Alzheimer's disease","volume":"15","author":"Sweeney","year":"2019","journal-title":"Alzheimers Dement"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1001\/jama.2017.3090","article-title":"Association between midlife vascular risk factors and estimated brain amyloid deposition","volume":"317","author":"Gottesman","year":"2017","journal-title":"JAMA"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"12906","DOI":"10.1038\/s41598-017-13349-5","article-title":"Altered neurovascular coupling as measured by optical imaging: a biomarker for Alzheimer\u2019s disease","volume":"7","author":"Kotliar","year":"2017","journal-title":"Sci Rep"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"897","DOI":"10.3233\/JAD-191028","article-title":"Influence of comorbidity of cerebrovascular disease and amyloid-\u03b2 on Alzheimer\u2019s disease","volume":"73","author":"Yassi","year":"2020","journal-title":"J Alzheimers Dis"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1159\/000487053","article-title":"Peripheral retinal imaging biomarkers for Alzheimer\u2019s disease: a pilot study","volume":"59","author":"Csincsik","year":"2018","journal-title":"Ophthalmic Res"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1177\/0271678X15606723","article-title":"Cerebral small vessel disease: capillary pathways to stroke and cognitive decline","volume":"36","author":"\u00d8stergaard","year":"2016","journal-title":"J Cereb Blood Flow Metab"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1136\/bjophthalmol-2017-310476","article-title":"Evaluation of optical coherence tomography angiographic findings in Alzheimer\u2019s type dementia","volume":"102","author":"Bulut","year":"2018","journal-title":"Br J Ophthalmol"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep learning\u2014a technology with the potential to transform health care","volume":"320","author":"Hinton","year":"2018","journal-title":"JAMA"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e806","DOI":"10.1016\/S2589-7500(22)00169-8","article-title":"A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study","volume":"4","author":"Cheung","year":"2022","journal-title":"Lancet Digital Health"},{"key":"ref20","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation[C]\/\/Medical image computing and computer-assisted intervention\u2013MICCAI 2015","author":"Ronneberger","year":"2015"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2748","DOI":"10.1109\/TMI.2020.3027665","article-title":"Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning","volume":"40","author":"Ding","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans Med Imaging"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/154860","article-title":"Robust vessel segmentation in fundus images","volume":"2013","author":"Budai","year":"2013","journal-title":"Int J Biomed Imaging"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","article-title":"IFCNN: a general image fusion framework based on convolutional neural network","volume":"54","author":"Zhang","year":"2020","journal-title":"Information Fusion"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1109\/JBHI.2022.3188710","article-title":"Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-030-87193-2_6","article-title":"Study group learning: improving retinal vessel segmentation trained with noisy labels","volume":"12901","author":"Yu","year":"2021","journal-title":"Med Image Comput Comput Assisted Intervent"},{"key":"ref27","author":"Zhuang","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"572484","DOI":"10.3389\/fnagi.2020.572484","article-title":"De-creased retinal vascular density in Alzheimer\u2019s disease (AD) and mild cognitive impairment (MCI): an optical coherence tomography angiography (OCTA) study","volume":"12","author":"Wang","year":"2021","journal-title":"Front Aging Neurosci"},{"key":"ref29","first-page":"167","article-title":"Hidden biometrics: When biometric security meets biomedical engineering","volume-title":"Retinal image processing in Biometrics[J]","author":"Kachouri","year":"2020"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2024.1449798\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T06:50:05Z","timestamp":1735887005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2024.1449798\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":29,"alternative-id":["10.3389\/fpubh.2024.1449798"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2024.1449798","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"article-number":"1449798"}}