{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T21:03:28Z","timestamp":1773781408696,"version":"3.50.1"},"reference-count":31,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":177,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advances in Human-Computer Interaction"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Alzheimer\u2019s disease (AD), a progressive neurodegenerative disorder whose symptoms become apparent late in the disease process but are only in the early stages of development, creates challenges that demand that this disorder be diagnosed early to reduce its progression. This research work has also suggested a lifelong learning system that insists on the combination of MRI and spike neural signals (EEG data) for the early detection of AD using automated deep learning. By recurrent catastrophic forgetting through elastic weight consolidation (EWC) and memory replay, the model learns from new data while retaining past knowledge that is vital in health\u2010care\u2010related environments where patient information is ever\u2010expanding. Three levels of integration between MRI and spike neural data have been used in this work: early, mid\u2010, and late fusion. Experimental results show that mid\u2010fusion gives better performance than other approaches of 86% accuracy, 84% sensitivity, and 88% specificity for AD identification, which can provide the structure of MRI and temporal EEG signals to identify the AD patient. Early fusion proved a capability to integrate general MRI\u2010EEG correspondences effectively, as integrated late fusion showed the capacity for handling diverse qualities of inputs by analyzing both models independently. The results of the study affirm the effectiveness of the continuous learning multimodal fusion strategy in improving both sensitivity to early AD biomarkers and data heterogeneity. The proposed approach seems to be promising for scalable and real\u2010time diagnostic solutions suitable to bring heterogeneous clinical data and the incremental nature of medical data into the diagnosis of early\u2010stage AD and patient management.<\/jats:p>","DOI":"10.1155\/ahci\/6632102","type":"journal-article","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T01:02:57Z","timestamp":1751072577000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Continuous Learning for Automated Early\u2010Stage Alzheimer\u2019s Detection Using MRI and Spike Neural Signals"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-369X","authenticated-orcid":false,"given":"Salar Jamal","family":"Abdulhameed Al-Atroshi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5204-2218","authenticated-orcid":false,"given":"Rana Layth","family":"Abdulazeez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6688-1918","authenticated-orcid":false,"given":"Shadan Mohammed","family":"Jihad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-4653","authenticated-orcid":false,"given":"Shahab Wahhab","family":"Kareem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.54097\/ajst.v8i1.14334"},{"key":"e_1_2_9_2_2","volume-title":"Journal of Physics: Conference Series","author":"Muhammed Raees P. C.","year":"2021"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2020.06.006"},{"key":"e_1_2_9_4_2","first-page":"16799","article-title":"Deep Learning-Based Approach for Multi-Stage Diagnosis of Alzheimer\u2019s Disease","volume":"83","author":"Ravi V.","year":"2024","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119129"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-53733-6"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105634"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.4103\/1673-5374.367840"},{"key":"e_1_2_9_9_2","first-page":"39","article-title":"Detection of Alzheimer\u2019s Disease and Dementia States Based on Deep Learning from MRI Images: a Comprehensive Review","volume":"1","author":"Altinkaya E.","year":"2020","journal-title":"Journal of the Institute of Electronics and Computer"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2023.3244952"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110934"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22979-3"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"SaralaR. BharathP. RajS. L. KumarM. S. andSrinivasM. H. Early-Stage Detection of Alzheimer\u2019s Disease Using MRI Scans with Deep Learning International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processinging August 2023 Springer Nature Switzerland Cham 147\u2013157.","DOI":"10.1007\/978-3-031-73065-8_12"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2024.0150545"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.3233\/jad-181049"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6690539"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-023-09625-7"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/jon.13063"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105215"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10222860"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2022.020866"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.966883"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106240"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"AlshammariM.andMezherM. A Modified Convolutional Neural Networks for MRI-Based Images for Detection and Stage Classification of Alzheimer Disease 2021 National Computing Colleges Conference (NCCC) March 2021 IEEE 1\u20137.","DOI":"10.1109\/NCCC49330.2021.9428810"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03284-4"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05652"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2022.102074"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110804"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.34172\/icnj.2022.20"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3174601"},{"key":"e_1_2_9_31_2","volume-title":"Challenges in Information, Communication and Computing Technology","author":"Vinoparkavi D.","year":"2025"}],"container-title":["Advances in Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/ahci\/6632102","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/ahci\/6632102","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/ahci\/6632102","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:40:26Z","timestamp":1773769226000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/ahci\/6632102"}},"subtitle":[],"editor":[{"given":"Burak","family":"Tasci","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/ahci\/6632102"],"URL":"https:\/\/doi.org\/10.1155\/ahci\/6632102","archive":["Portico"],"relation":{},"ISSN":["1687-5893","1687-5907"],"issn-type":[{"value":"1687-5893","type":"print"},{"value":"1687-5907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-02-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6632102"}}