{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:06:30Z","timestamp":1791522390585,"version":"4.3.4"},"reference-count":22,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100019345","name":"King Salman Center for Disability Research","doi-asserted-by":"publisher","award":["KSRG\u20102024\u2010430"],"award-info":[{"award-number":["KSRG\u20102024\u2010430"]}],"id":[{"id":"10.13039\/501100019345","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep learning frameworks to improve predictive accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>A novel framework was developed, including trained models for structured data and magnetic resonance images. The structured data model used a long short\u2010term memory (LSTM) for temporal dependencies and a feedforward neural network (FNN) for static patterns. The MRI\u2010based model employed ResNet50 and MobileNetV2 to extract spatial features. Models were applied on National Alzheimer's Coordinating Centre (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets and compared to previous works.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The MRI\u2010based model achieved 96.19% accuracy on the ADNI dataset, while the hybrid model attained 99.82% accuracy on NACC dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>This study highlights hybrid AI models' potential in AD detection, enabling earlier interventions and improved detection outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>AI models were explored for early AD detection using NACC and ADNI datasets.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Achieved high accuracy with LSTM on NACC data, showing potential for early AD diagnosis.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Evaluated transfer learning models (MobileNetV2, ResNet\u201050) to address data limitations.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>A method is proposed for the careful validation of transfer learning models in medical brain diagnostics.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.70175","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T23:54:11Z","timestamp":1747094051000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Early detection of Alzheimer's disease using deep learning methods"],"prefix":"10.1002","volume":"21","author":[{"given":"Anthony Chidubem","family":"Mmadumbu","sequence":"first","affiliation":[{"name":"College of Computing Birmingham City University  Birmingham UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-1708","authenticated-orcid":false,"given":"Faisal","family":"Saeed","sequence":"additional","affiliation":[{"name":"College of Computing Birmingham City University  Birmingham UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuad","family":"Ghaleb","sequence":"additional","affiliation":[{"name":"College of Computing Birmingham City University  Birmingham UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan Noman","family":"Qasem","sequence":"additional","affiliation":[{"name":"Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU)  Riyadh Saudi Arabia"},{"name":"King Salman Center for Disability Research  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201020674\u2010x"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23116079"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms252010911"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000001041"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22052761"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010024\u201051985\u2010w"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.062076"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10080950"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"PatraB MaityNP Charan SutarB Early detection of Alzheimer's disease using feed forward neural network. In:2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT).IEEE;2023:1\u20104. doi:10.1109\/ICCCNT56998.2023.10307151","DOI":"10.1109\/ICCCNT56998.2023.10307151"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618\u2010024\u201001062\u20107"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12102290"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12621"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11081473"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.44937"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110934"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.3233\/JAD\u2010200888"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"Durga SupriyaHL ThomasSM Sowmya KamathS.A multimodal approach integrating convolutional and recurrent neural networks for Alzheimer's disease temporal progression prediction. 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