{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T14:10:00Z","timestamp":1789135800597,"version":"build-2803163510"},"reference-count":173,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92461308"],"award-info":[{"award-number":["92461308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U25A20566"],"award-info":[{"award-number":["U25A20566"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82027806"],"award-info":[{"award-number":["82027806"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372220"],"award-info":[{"award-number":["82372220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["92461308"],"award-info":[{"award-number":["92461308"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U25A20566"],"award-info":[{"award-number":["U25A20566"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82027806"],"award-info":[{"award-number":["82027806"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82372220"],"award-info":[{"award-number":["82372220"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment.<\/jats:p>","DOI":"10.3390\/bios16090514","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:29:29Z","timestamp":1789133369000},"page":"514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Alzheimer\u2019s Disease Diagnosis: From Traditional Machine Learning to Large Language Models"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiayao","family":"Guo","sequence":"first","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqi","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-764X","authenticated-orcid":false,"given":"Hongde","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6882-7774","authenticated-orcid":false,"given":"Xuemei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S0140-6736(15)01124-1","article-title":"Alzheimer\u2019s Disease","volume":"388","author":"Scheltens","year":"2016","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e105","DOI":"10.1016\/S2468-2667(21)00249-8","article-title":"Estimation of the Global Prevalence of Dementia in 2019 and Forecasted Prevalence in 2050: An Analysis for the Global Burden of Disease Study 2019","volume":"7","author":"Nichols","year":"2022","journal-title":"Lancet Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jalz.2012.11.006","article-title":"The Worldwide Economic Impact of Dementia 2010","volume":"9","author":"Wimo","year":"2013","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/S1474-4422(05)70168-X","article-title":"Preclinical Alzheimer\u2019s Disease: Diagnosis and Prediction of Progression","volume":"4","author":"Chong","year":"2005","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The Neuropathological Diagnosis of Alzheimer\u2019s Disease","volume":"14","author":"DeTure","year":"2019","journal-title":"Mol. Neurodegener."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.jalz.2010.03.013","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative Biomarkers as Quantitative Phenotypes: Genetics Core Aims, Progress, and Plans","volume":"6","author":"Saykin","year":"2010","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neurobiolaging.2019.09.008","article-title":"What Electrophysiology Tells Us about Alzheimer\u2019s Disease: A Window into the Synchronization and Connectivity of Brain Neurons","volume":"85","author":"Babiloni","year":"2020","journal-title":"Neurobiol. Aging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101346","DOI":"10.1016\/j.arr.2021.101346","article-title":"Omics Sciences for Systems Biology in Alzheimer\u2019s Disease: State-of-the-Art of the Evidence","volume":"69","author":"Hampel","year":"2021","journal-title":"Ageing Res. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41746-024-01043-6","article-title":"Informing Immunotherapy with Multi-Omics Driven Machine Learning","volume":"7","author":"Li","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","article-title":"Artificial Intelligence for Multimodal Data Integration in Oncology","volume":"40","author":"Lipkova","year":"2022","journal-title":"Cancer Cell"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s12559-021-09946-2","article-title":"Deep Learning Approach for Early Detection of Alzheimer\u2019s Disease","volume":"14","author":"Helaly","year":"2022","journal-title":"Cogn. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"bbag110","DOI":"10.1093\/bib\/bbag110","article-title":"Large Language Model Agents for Biological Intelligence across Genomics, Proteomics, Spatial Biology, and Biomedicine","volume":"27","author":"Dip","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S1474-4422(09)70299-6","article-title":"Hypothetical Model of Dynamic Biomarkers of the Alzheimer\u2019s Pathological Cascade","volume":"9","author":"Jack","year":"2010","journal-title":"Lancet Neurol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1038\/s41467-021-21057-y","article-title":"Asymmetric Thinning of the Cerebral Cortex across the Adult Lifespan Is Accelerated in Alzheimer\u2019s Disease","volume":"12","author":"Roe","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, J., Jeong, M., Stiles, W.R., and Choi, H.S. (2022). Neuroimaging Modalities in Alzheimer\u2019s Disease: Diagnosis and Clinical Features. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23116079"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.bbrc.2021.11.093","article-title":"Neuronal PET Tracers for Alzheimer\u2019s Disease","volume":"587","author":"Singh","year":"2022","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s11307-009-0247-7","article-title":"Surface Feature-Guided Mapping of Cerebral Metabolic Changes in Cognitively Normal and Mildly Impaired Elderly","volume":"12","author":"Apostolova","year":"2010","journal-title":"Mol. Imaging Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/s41398-025-03550-w","article-title":"Fueling the Brain\u2014The Role of Apolipoprotein E in Brain Energy Metabolism and Its Implications for Alzheimer\u2019s Disease","volume":"15","author":"Budny","year":"2025","journal-title":"Transl. Psychiatry"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1650025","DOI":"10.1142\/S0129065716500258","article-title":"Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer\u2019s Disease","volume":"26","author":"Ortiz","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Houmani, N., Vialatte, F., Gallego-Jutgl\u00e0, E., Dreyfus, G., Nguyen-Michel, V.-H., Mariani, J., and Kinugawa, K. (2018). Diagnosis of Alzheimer\u2019s Disease with Electroencephalography in a Differential Framework. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0193607"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ijpsycho.2007.11.002","article-title":"Quantitative EEG in Early Alzheimer\u2019s Disease Patients\u2014Power Spectrum and Complexity Features","volume":"68","author":"Czigler","year":"2008","journal-title":"Int. J. Psychophysiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1550032","DOI":"10.1142\/S012906571550032X","article-title":"Epoch-Based Entropy for Early Screening of Alzheimer\u2019s Disease","volume":"25","author":"Houmani","year":"2015","journal-title":"Int. J. Neural Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neurobiolaging.2017.06.009","article-title":"Cerebral PET Glucose Hypometabolism in Subjects with Mild Cognitive Impairment and Higher EEG High-Alpha\/Low-Alpha Frequency Power Ratio","volume":"58","author":"Moretti","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/s13195-025-01765-z","article-title":"Digital Twins and Non-Invasive Recordings Enable Early Diagnosis of Alzheimer\u2019s Disease","volume":"17","author":"Amato","year":"2025","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TBME.2021.3049199","article-title":"A Graph Gaussian Embedding Method for Predicting Alzheimer\u2019s Disease Progression with MEG Brain Networks","volume":"68","author":"Xu","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e70835","DOI":"10.1002\/alz.70835","article-title":"Tracking Electrophysiological Signatures of Alzheimer\u2019s Disease: A Systematic Review of Multimodal Studies","volume":"21","author":"Sohrabpour","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s00018-022-04614-6","article-title":"Omics-Based Biomarkers Discovery for Alzheimer\u2019s Disease","volume":"79","author":"Aerqin","year":"2022","journal-title":"Cell. Mol. Life Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s41588-018-0311-9","article-title":"Genome-Wide Meta-Analysis Identifies New Loci and Functional Pathways Influencing Alzheimer\u2019s Disease Risk","volume":"51","author":"Jansen","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e246210","DOI":"10.6064\/2012\/246210","article-title":"The Genetics of Alzheimer\u2019s Disease","volume":"2012","author":"Barber","year":"2012","journal-title":"Scientifica"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/S1474-4422(16)00127-7","article-title":"Genetic Variations Underlying Alzheimer\u2019s Disease: Evidence from Genome-Wide Association Studies and Beyond","volume":"15","author":"Cuyvers","year":"2016","journal-title":"Lancet Neurol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1038\/349704a0","article-title":"Segregation of a Missense Mutation in the Amyloid Precursor Protein Gene with Familial Alzheimer\u2019s Disease","volume":"349","author":"Goate","year":"1991","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s11910-013-0381-0","article-title":"Genome-Wide Association Studies in Alzheimer\u2019s Disease: A Review","volume":"13","author":"Tosto","year":"2013","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e286","DOI":"10.1212\/NXG.0000000000000286","article-title":"Rare Genetic Variation Implicated in Non-Hispanic White Families with Alzheimer Disease","volume":"4","author":"Beecham","year":"2018","journal-title":"Neurol. Genet."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1038\/s41380-018-0112-7","article-title":"Whole Exome Sequencing Study Identifies Novel Rare and Common Alzheimer\u2019s-Associated Variants Involved in Immune Response and Transcriptional Regulation","volume":"25","author":"Bis","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000485503","article-title":"Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer\u2019s Disease Sequencing Project","volume":"45","author":"Blue","year":"2018","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1038\/s41588-024-01793-9","article-title":"Synthetic Surrogates Improve Power for Genome-Wide Association Studies of Partially Missing Phenotypes in Population Biobanks","volume":"56","author":"McCaw","year":"2024","journal-title":"Nat. Genet."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1038\/s41588-024-01831-6","article-title":"Unsupervised Representation Learning on High-Dimensional Clinical Data Improves Genomic Discovery and Prediction","volume":"56","author":"Yun","year":"2024","journal-title":"Nat. Genet."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s13148-020-00864-y","article-title":"Harnessing Peripheral DNA Methylation Differences in the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) to Reveal Novel Biomarkers of Disease","volume":"12","author":"Vasanthakumar","year":"2020","journal-title":"Clin. Epigenet."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.arr.2013.05.003","article-title":"Epigenetic Mechanisms in Alzheimer\u2019s Disease: Implications for Pathogenesis and Therapy","volume":"12","author":"Wang","year":"2013","journal-title":"Ageing Res. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1038\/s41593-018-0253-7","article-title":"A Histone Acetylome-Wide Association Study of Alzheimer\u2019s Disease Identifies Disease-Associated H3K27ac Differences in the Entorhinal Cortex","volume":"21","author":"Marzi","year":"2018","journal-title":"Nat. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1038\/s41588-020-0696-0","article-title":"An Integrated Multi-Omics Approach Identifies Epigenetic Alterations Associated with Alzheimer\u2019s Disease","volume":"52","author":"Nativio","year":"2020","journal-title":"Nat. Genet."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17394","DOI":"10.1038\/s41598-018-35789-3","article-title":"Systematic Analysis and Biomarker Study for Alzheimer\u2019s Disease","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.ncrna.2024.03.008","article-title":"The Roles of Long Non-Coding RNAs in Alzheimer\u2019s Disease Diagnosis, Treatment, and Their Involvement in Alzheimer\u2019s Disease Immune Responses","volume":"9","author":"Wu","year":"2024","journal-title":"Non-Coding RNA Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112681","DOI":"10.1016\/j.biopha.2022.112681","article-title":"MicroRNAs in Alzheimer\u2019s Disease: Potential Diagnostic Markers and Therapeutic Targets","volume":"148","author":"Liu","year":"2022","journal-title":"Biomed. Pharmacother."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhao, T., Chu, X., and Cheng, Z. (2023). Development of a Diagnostic and Risk Prediction Model for Alzheimer\u2019s Disease through Integration of Single-Cell and Bulk Transcriptomic Analysis of Glutamine Metabolism. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1275793"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Krokidis, M.G., Vrahatis, A.G., Lazaros, K., and Vlamos, P. (2023). Exploring Promising Biomarkers for Alzheimer\u2019s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data. Appl. Sci., 13.","DOI":"10.3390\/app13095553"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8652","DOI":"10.3390\/cimb45110544","article-title":"Machine Learning Analysis of Alzheimer\u2019s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions","volume":"45","author":"Krokidis","year":"2023","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1093\/arclin\/acae019","article-title":"Plasma Biomarkers for Alzheimer\u2019s and Related Dementias: A Review and Outlook for Clinical Neuropsychology","volume":"39","author":"Dark","year":"2024","journal-title":"Arch. Clin. Neuropsychol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3479","DOI":"10.1021\/acschemneuro.9b00095","article-title":"Potential Value of Plasma Amyloid-\u03b2, Total Tau, and Neurofilament Light for Identification of Early Alzheimer\u2019s Disease","volume":"10","author":"Shi","year":"2019","journal-title":"ACS Chem. Neurosci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13578-023-01189-y","article-title":"Abnormal Protein Post-Translational Modifications Induces Aggregation and Abnormal Deposition of Protein, Mediating Neurodegenerative Diseases","volume":"14","author":"Li","year":"2024","journal-title":"Cell Biosci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mahajan, U.V., Varma, V.R., Griswold, M.E., Blackshear, C.T., An, Y., Oommen, A.M., Varma, S., Troncoso, J.C., Pletnikova, O., and O\u2019Brien, R. (2020). Dysregulation of Multiple Metabolic Networks Related to Brain Transmethylation and Polyamine Pathways in Alzheimer Disease: A Targeted Metabolomic and Transcriptomic Study. PLoS Med., 17.","DOI":"10.1371\/journal.pmed.1003439"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e4875","DOI":"10.1002\/bmc.4875","article-title":"Serum Metabolomic Profiling in Patients with Alzheimer Disease and Amnestic Mild Cognitive Impairment by GC\/MS","volume":"34","author":"Sun","year":"2020","journal-title":"Biomed. Chromatogr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.3233\/JAD-200305","article-title":"A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer\u2019s Disease Using Artificial Intelligence and Machine Learning","volume":"78","author":"Yilmaz","year":"2020","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/nm.3466","article-title":"Plasma Phospholipids Identify Antecedent Memory Impairment in Older Adults","volume":"20","author":"Mapstone","year":"2014","journal-title":"Nat. Med."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wilkins, J.M., and Trushina, E. (2018). Application of Metabolomics in Alzheimer\u2019s Disease. Front. Neurol., 8.","DOI":"10.3389\/fneur.2017.00719"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1001\/jamaneurol.2017.4198","article-title":"Associations of the Top 20 Alzheimer Disease Risk Variants With Brain Amyloidosis","volume":"75","author":"Apostolova","year":"2018","journal-title":"JAMA Neurol."},{"key":"ref_57","first-page":"e12308","article-title":"For The Alzheimer\u2019s Disease Neuroimaging Initiative. Association of the Top 20 Alzheimer\u2019s Disease Risk Genes with [18F]Flortaucipir PET","volume":"14","author":"Stage","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_58","first-page":"53","article-title":"The Effect of the Top 20 Alzheimer Disease Risk Genes on Gray-Matter Density and FDG PET Brain Metabolism","volume":"5","author":"Stage","year":"2016","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"13752","DOI":"10.1038\/s41598-023-39737-8","article-title":"Integration of Plasma and CSF Metabolomics with CSF Proteomic Reveals Novel Associations between Lipid Mediators and Central Nervous System Vascular and Energy Metabolism","volume":"13","author":"Borkowski","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1002\/alz.12961","article-title":"Multiomics Profiling of Human Plasma and Cerebrospinal Fluid Reveals ATN-Derived Networks and Highlights Causal Links in Alzheimer\u2019s Disease","volume":"19","author":"Shi","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/s41467-022-28280-1","article-title":"Inferring Protein Expression Changes from mRNA in Alzheimer\u2019s Dementia Using Deep Neural Networks","volume":"13","author":"Tasaki","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S1474-4422(19)30356-4","article-title":"The Gut Microbiome in Neurological Disorders","volume":"19","author":"Cryan","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s40035-022-00323-z","article-title":"Gut Microbiome, Cognitive Function and Brain Structure: A Multi-Omics Integration Analysis","volume":"11","author":"Liang","year":"2022","journal-title":"Transl. Neurodegener."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s40035-022-00315-z","article-title":"A Review of Brain Imaging Biomarker Genomics in Alzheimer\u2019s Disease: Implementation and Perspectives","volume":"11","author":"Li","year":"2022","journal-title":"Transl. Neurodegener."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"100695","DOI":"10.1016\/j.medj.2025.100695","article-title":"The Impact of Real-World Evidence in Implementing and Optimizing Alzheimer\u2019s Disease Care","volume":"6","author":"Hampel","year":"2025","journal-title":"Med"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1080\/13803395.2018.1446513","article-title":"Connected Speech and Language in Mild Cognitive Impairment and Alzheimer\u2019s Disease: A Review of Picture Description Tasks","volume":"40","author":"Mueller","year":"2018","journal-title":"J. Clin. Exp. Neuropsychol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3233\/JAD-170881","article-title":"Connected Speech Features from Picture Description in Alzheimer\u2019s Disease: A Systematic Review","volume":"65","author":"Slegers","year":"2018","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1093\/jamia\/ocaa174","article-title":"A Systematic Literature Review of Automatic Alzheimer\u2019s Disease Detection from Speech and Language","volume":"27","author":"Petti","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Szatloczki, G., Hoffmann, I., Vincze, V., Kalman, J., and Pakaski, M. (2015). Speaking in Alzheimer\u2019s Disease, Is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer\u2019s Disease. Front. Aging Neurosci., 7.","DOI":"10.3389\/fnagi.2015.00195"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Vigo, I., Coelho, L., and Reis, S. (2022). Speech-and Language-Based Classification of Alzheimer\u2019s Disease: A Systematic Review. Bioengineering, 9.","DOI":"10.3390\/bioengineering9010027"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1038\/s44222-026-00433-7","article-title":"A Framework of Digital Biomarkers for Neurodegenerative Diseases","volume":"4","author":"Nerrise","year":"2026","journal-title":"Nat. Rev. Bioeng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1001\/jama.2014.17841","article-title":"Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data","volume":"313","author":"Case","year":"2015","journal-title":"JAMA"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2466\/10.22.PMS.119c12z3","article-title":"Association between Finger Tapping, Attention, Memory, and Cognitive Diagnosis in Elderly Patients","volume":"119","author":"Rabinowitz","year":"2014","journal-title":"Percept. Mot. Ski."},{"key":"ref_74","first-page":"540","article-title":"Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking","volume":"15","author":"Jo","year":"2016","journal-title":"J. Sports Sci. Med."},{"key":"ref_75","first-page":"458","article-title":"Consumer Sleep Tracking Devices: A Critical Review","volume":"Volume 210","author":"Jeon","year":"2015","journal-title":"Digital Healthcare Empowering Europeans: Proceedings of MIE2015"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Nam, U., Lee, K., Ko, H., Lee, J.-Y., and Lee, E.C. (2020). Analyzing Facial and Eye Movements to Screen for Alzheimer\u2019s Disease. Sensors, 20.","DOI":"10.3390\/s20185349"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1021\/acs.analchem.5c06515","article-title":"Dopamine Self-Polymerization-Assisted Oriented Antibody Immobilization on a Transistor for Alzheimer\u2019s Disease Diagnosis","volume":"98","author":"Gong","year":"2026","journal-title":"Anal. Chem."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1021\/acsnano.3c09311","article-title":"High-Performance Plasma Biomarker Panel for Alzheimer\u2019s Disease Screening Using a Femtomolar-Level Label-Free Biosensing System","volume":"18","author":"Wang","year":"2024","journal-title":"ACS Nano"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6426","DOI":"10.1021\/acsnano.2c00538","article-title":"Rapid Biomarker Screening of Alzheimer\u2019s Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy","volume":"16","author":"Wang","year":"2022","journal-title":"ACS Nano"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4390","DOI":"10.1021\/acschemneuro.4c00369","article-title":"Ultrasensitive Detection of Blood-Based Alzheimer\u2019s Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning","volume":"15","author":"Resmi","year":"2024","journal-title":"ACS Chem. Neurosci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"28757","DOI":"10.1021\/acsanm.4c06056","article-title":"Approach for Label-Free and High-Accuracy Identification of Alzheimer\u2019s Disease Combining Machine Learning and SERS Technology","volume":"7","author":"Lu","year":"2024","journal-title":"ACS Appl. Nano Mater."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41746-019-0084-2","article-title":"Digital Biomarkers for Alzheimer\u2019s Disease: The Mobile\/Wearable Devices Opportunity","volume":"2","author":"Kourtis","year":"2019","journal-title":"npj Digit. Med."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"105348","DOI":"10.1016\/j.cmpb.2020.105348","article-title":"A Survey on Machine and Statistical Learning for Longitudinal Analysis of Neuroimaging Data in Alzheimer\u2019s Disease","volume":"189","author":"Piella","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Dara, O.A., Lopez-Guede, J.M., Raheem, H.I., Rahebi, J., Zulueta, E., and Fernandez-Gamiz, U. (2023). Alzheimer\u2019s Disease Diagnosis Using Machine Learning: A Survey. Appl. Sci., 13.","DOI":"10.3390\/app13148298"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5905","DOI":"10.1002\/alz.13427","article-title":"Artificial Intelligence for Dementia Genetics and Omics","volume":"19","author":"Bettencourt","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"5885","DOI":"10.1002\/alz.13412","article-title":"Artificial Intelligence for Diagnostic and Prognostic Neuroimaging in Dementia: A Systematic Review","volume":"19","author":"Borchert","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1515\/revneuro-2023-0033","article-title":"A Review of Neuroimaging-Based Data-Driven Approach for Alzheimer\u2019s Disease Heterogeneity Analysis","volume":"35","author":"Liu","year":"2024","journal-title":"Rev. Neurosci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1093\/brain\/awz384","article-title":"A Multiomics Approach to Heterogeneity in Alzheimer\u2019s Disease: Focused Review and Roadmap","volume":"143","author":"Badhwar","year":"2020","journal-title":"Brain"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Long, X., Chen, L., Jiang, C., Zhang, L., and Initiative, A.D.N. (2017). Prediction and Classification of Alzheimer Disease Based on Quantification of MRI Deformation. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0173372"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1097\/JCMA.0000000000001188","article-title":"Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease","volume":"88","author":"Aslan","year":"2024","journal-title":"J. Chin. Med. Assoc."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"13236","DOI":"10.1038\/s41598-018-31653-6","article-title":"Machine Learning Identified an Alzheimer\u2019s Disease-Related FDG-PET Pattern Which Is Also Expressed in Lewy Body Dementia and Parkinson\u2019s Disease Dementia","volume":"8","author":"Katako","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Bi, X., Shu, Q., Sun, Q., and Xu, Q. (2018). Random Support Vector Machine Cluster Analysis of Resting-State fMRI in Alzheimer\u2019s Disease. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194479"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.3233\/JAD-200171","article-title":"Classification of Alzheimer\u2019s Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation","volume":"75","author":"Vecchio","year":"2020","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.nicl.2017.10.009","article-title":"EEG Oscillations during Word Processing Predict MCI Conversion to Alzheimer\u2019s Disease","volume":"17","author":"Mazaheri","year":"2018","journal-title":"Neuroimage Clin."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"De Velasco Oriol, J., Vallejo, E.E., Estrada, K., Tam\u00e9z Pe\u00f1a, J.G., and Disease Neuroimaging Initiative, T.A. (2019). Benchmarking Machine Learning Models for Late-Onset Alzheimer\u2019s Disease Prediction from Genomic Data. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3158-x"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Kononikhin, A.S., Zakharova, N.V., Semenov, S.D., Bugrova, A.E., Brzhozovskiy, A.G., Indeykina, M.I., Fedorova, Y.B., Kolykhalov, I.V., Strelnikova, P.A., and Ikonnikova, A.Y. (2022). Prognosis of Alzheimer\u2019s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23147907"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.csl.2018.07.007","article-title":"Identifying Mild Cognitive Impairment and Mild Alzheimer\u2019s Disease Based on Spontaneous Speech Using ASR and Linguistic Features","volume":"53","author":"Gosztolya","year":"2019","journal-title":"Comput. Speech Lang."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"130","DOI":"10.2174\/1567205014666171121114930","article-title":"A Speech Recognition-Based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech","volume":"15","author":"Hoffmann","year":"2018","journal-title":"Curr. Alzheimer Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s13195-023-01268-9","article-title":"Quantification of Identifying Cognitive Impairment Using Olfactory-Stimulated Functional near-Infrared Spectroscopy with Machine Learning: A Post Hoc Analysis of a Diagnostic Trial and Validation of an External Additional Trial","volume":"15","author":"Kim","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"46925","DOI":"10.1109\/ACCESS.2025.3550855","article-title":"Advancements in Speech Recognition: A Systematic Review of Deep Learning Transformer Models, Trends, Innovations, and Future Directions","volume":"13","author":"Sharrab","year":"2025","journal-title":"IEEE Access"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"101614","DOI":"10.1016\/j.arr.2022.101614","article-title":"Artificial Intelligence in Brain MRI Analysis of Alzheimer\u2019s Disease over the Past 12 Years: A Systematic Review","volume":"77","author":"Frizzell","year":"2022","journal-title":"Ageing Res. Rev."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13195-022-01131-3","article-title":"Deep Learning-Based Speech Analysis for Alzheimer\u2019s Disease Detection: A Literature Review","volume":"14","author":"Yang","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Lan, A.Y., and Corces, M.R. (2022). Deep Learning Approaches for Noncoding Variant Prioritization in Neurodegenerative Diseases. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.1027224"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/s41551-017-0178-6","article-title":"Prediction of Off-Target Activities for the End-to-End Design of CRISPR Guide RNAs","volume":"2","author":"Listgarten","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/s42256-020-0201-6","article-title":"Deep Learning Decodes the Principles of Differential Gene Expression","volume":"2","author":"Tasaki","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3390\/make5020035","article-title":"A Survey of Deep Learning for Alzheimer\u2019s Disease","volume":"5","author":"Zhou","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/19466315.2021.1884129","article-title":"Deep Learning Based Multimodal Progression Modeling for Alzheimer\u2019s Disease","volume":"13","author":"Yang","year":"2021","journal-title":"Stat. Biopharm. Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"40057","DOI":"10.1007\/s11042-023-16928-z","article-title":"Artificial Intelligence Techniques for the Effective Diagnosis of Alzheimer\u2019s Disease: A Review","volume":"83","author":"Sanjay","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s13059-023-02916-x","article-title":"A Graph Neural Network-Based Interpretable Framework Reveals a Novel DNA Fragility\u2013Associated Chromatin Structural Unit","volume":"24","author":"Sun","year":"2023","journal-title":"Genome Biol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41416-024-02706-7","article-title":"Graph Machine Learning for Integrated Multi-Omics Analysis","volume":"131","author":"Valous","year":"2024","journal-title":"Br. J. Cancer"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1109\/JBHI.2023.3284794","article-title":"Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis","volume":"27","author":"Xiao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1186\/s12967-024-05976-0","article-title":"Integration of Graph Neural Networks and Multi-Omics Analysis Identify the Predictive Factor and Key Gene for Immunotherapy Response and Prognosis of Bladder Cancer","volume":"22","author":"Ren","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"106792","DOI":"10.1016\/j.neunet.2024.106792","article-title":"Graph Neural Networks for Electroencephalogram Analysis: Alzheimer\u2019s Disease and Epilepsy Use Cases","volume":"181","author":"Abadal","year":"2025","journal-title":"Neural Netw."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"10466","DOI":"10.1109\/TPAMI.2024.3443141","article-title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","volume":"46","author":"Jin","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Lyu, R. (2026). Deep Learning Approaches for EEG-Based Healthcare Applications: A Comprehensive Review. Front. Hum. Neurosci., 19.","DOI":"10.3389\/fnhum.2025.1689073"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.aej.2023.09.050","article-title":"Time-Series Visual Explainability for Alzheimer\u2019s Disease Progression Detection for Smart Healthcare","volume":"82","author":"Rahim","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., and Song, X. (2020). Early Detection of Alzheimer\u2019s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.00259"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","article-title":"Deep Learning Framework for Alzheimer\u2019s Disease Diagnosis via 3D-CNN and FSBi-LSTM","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"223","DOI":"10.55525\/tjst.1396312","article-title":"Use of 3D-CAPSNET and RNN Models for 4D fMRI-Based Alzheimer\u2019s Disease Pre-Detection","volume":"19","author":"Dalveren","year":"2024","journal-title":"Turk. J. Sci. Technol."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Tufail, A.B., Anwar, N., Othman, M.T.B., Ullah, I., Khan, R.A., Ma, Y.-K., Adhikari, D., Rehman, A.U., Shafiq, M., and Hamam, H. (2022). Early-Stage Alzheimer\u2019s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. Sensors, 22.","DOI":"10.3390\/s22124609"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"046087","DOI":"10.1088\/1741-2552\/ac05d8","article-title":"Deep Learning of Resting-State Electroencephalogram Signals for Three-Class Classification of Alzheimer\u2019s Disease, Mild Cognitive Impairment and Healthy Ageing","volume":"18","author":"Huggins","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"105298","DOI":"10.1016\/j.bspc.2023.105298","article-title":"Alzheimer\u2019s Diseases Diagnosis Using Fusion of High Informative BiLSTM and CNN Features of EEG Signal","volume":"86","author":"Imani","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"110057","DOI":"10.1016\/j.jneumeth.2024.110057","article-title":"Deep Insights into MCI Diagnosis: A Comparative Deep Learning Analysis of EEG Time Series","volume":"403","year":"2024","journal-title":"J. Neurosci. Methods"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TNSRE.2022.3204913","article-title":"EEG-Based Graph Neural Network Classification of Alzheimer\u2019s Disease: An Empirical Evaluation of Functional Connectivity Methods","volume":"30","author":"Klepl","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_125","unstructured":"Wang, Z., Bao, R., Wu, Y., Liu, G., Yang, L., Zhan, L., Zheng, F., Jiang, W., and Zhang, Y. (2024). A Self-Guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer\u2019s Diseases. arXiv."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1016\/j.csbj.2023.02.021","article-title":"Deep Belief Network-Based Approach for Detecting Alzheimer\u2019s Disease Using the Multi-Omics Data","volume":"21","author":"Mahendran","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"bbae658","DOI":"10.1093\/bib\/bbae658","article-title":"MORE: A Multi-Omics Data-Driven Hypergraph Integration Network for Biomedical Data Classification and Biomarker Identification","volume":"26","author":"Wang","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"92294","DOI":"10.1109\/ACCESS.2022.3203068","article-title":"An Evaluation on Information Composition in Dementia Detection Based on Speech","volume":"10","author":"Zheng","year":"2022","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Lin, S.-Y., Chang, H.-L., Hwang, J.-J., Wai, T., Chang, Y.-L., and Fu, L.-C. (2022). Automatic Audio-Based Screening System for Alzheimer\u2019s Disease Detection. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC53654.2022.9945127"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Yu, X., Srivastava, S., Huang, S., Hayden, E.Y., Teplow, D.B., and Xie, Y.-H. (2022). The Feasibility of Early Alzheimer\u2019s Disease Diagnosis Using a Neural Network Hybrid Platform. Biosensors, 12.","DOI":"10.3390\/bios12090753"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal Machine Learning: A Survey and Taxonomy","volume":"41","author":"Ahuja","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"20150571","DOI":"10.1098\/rsif.2015.0571","article-title":"Methods for Biological Data Integration: Perspectives and Challenges","volume":"12","year":"2015","journal-title":"J. R. Soc. Interface"},{"key":"ref_133","first-page":"325","article-title":"A Review on Machine Learning Principles for Multi-View Biological Data Integration","volume":"19","author":"Li","year":"2018","journal-title":"Brief. Bioinform."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","article-title":"Integration Strategies of Multi-Omics Data for Machine Learning Analysis","volume":"19","author":"Picard","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1038\/s41467-022-31037-5","article-title":"Multimodal Deep Learning for Alzheimer\u2019s Disease Dementia Assessment","volume":"13","author":"Qiu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"100817","DOI":"10.1016\/j.crmeth.2024.100817","article-title":"Cross-Attention Enables Deep Learning on Limited Omics-Imaging-Clinical Data of 130 Lung Cancer Patients","volume":"4","author":"Verma","year":"2024","journal-title":"Cell Rep. Methods"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"29038","DOI":"10.1038\/s41598-025-14998-7","article-title":"Multimodal Alzheimer\u2019s Disease Recognition from Image, Text and Audio","volume":"15","author":"Lee","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"5210","DOI":"10.1038\/s41598-024-56001-9","article-title":"Automated Detection of Alzheimer\u2019s Disease: A Multi-Modal Approach with 3D MRI and Amyloid PET","volume":"14","author":"Castellano","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Cai, H., Huang, X., Liu, Z., Liao, W., Dai, H., Wu, Z., Zhu, D., Ren, H., Li, Q., and Liu, T. (2023). Exploring Multimodal Approaches for Alzheimer\u2019s Disease Detection Using Patient Speech Transcript and Audio Data. arXiv.","DOI":"10.1007\/978-3-031-43075-6_34"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12938-024-01204-4","article-title":"Multimodal Diagnosis Model of Alzheimer\u2019s Disease Based on Improved Transformer","volume":"23","author":"Tang","year":"2024","journal-title":"Biomed. Eng. Online"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"e07629","DOI":"10.1002\/advs.202507629","article-title":"Integration of Genetic and Imaging Data for Alzheimer\u2019s Disease Diagnosis and Interpretation","volume":"12","author":"Wang","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"121550","DOI":"10.1016\/j.neuroimage.2025.121550","article-title":"Multimodal Integration of Plasma Biomarkers, MRI, and Genetic Risk to Predict Cerebral Amyloid Burden in Alzheimer\u2019s Disease","volume":"322","author":"Wang","year":"2025","journal-title":"Neuroimage"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"114739","DOI":"10.1016\/j.asoc.2026.114739","article-title":"A Multimodal Alzheimer\u2019s Disease Diagnosis Model with Fusion of Cross-Modal Attention and Graph Attention","volume":"192","author":"Liang","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"108621","DOI":"10.1016\/j.compbiomed.2024.108621","article-title":"Comparative Analysis of Machine Learning Algorithms for Alzheimer\u2019s Disease Classification Using EEG Signals and Genetic Information","volume":"176","author":"Yu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"111879","DOI":"10.1016\/j.compbiomed.2026.111879","article-title":"NeuroOmics-Net: An Interpretable Multimodal Deep Learning Framework for Alzheimer\u2019s Disease Diagnosis and Progression Prediction Using Neuroimaging, EEG, and Genomic Data","volume":"214","author":"Kavitha","year":"2026","journal-title":"Comput. Biol. Med."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Chen, D., Zhang, L., and Ma, C. (2020). A Multimodal Diagnosis Predictive Model of Alzheimer\u2019s Disease with Few-Shot Learning. Proceedings of the 2020 International Conference on Public Health and Data Science (ICPHDS), IEEE.","DOI":"10.1109\/ICPHDS51617.2020.00060"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/TNSRE.2025.3561043","article-title":"A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer\u2019s Disease with Fusion Network Using Eye Movements and Speech","volume":"33","author":"Shah","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Balagopalan, A., Eyre, B., Robin, J., Rudzicz, F., and Novikova, J. (2021). Comparing Pre-Trained and Feature-Based Models for Prediction of Alzheimer\u2019s Disease Based on Speech. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.635945"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Yuan, J., Cai, X., and Church, K. (2021). Pause-Encoded Language Models for Recognition of Alzheimer\u2019s Disease and Emotion. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP39728.2021.9413548"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.21437\/Interspeech.2020-2516","article-title":"Disfluencies and Fine-Tuning Pre-Trained Language Models for Detection of Alzheimer\u2019s Disease","volume":"Volume 2020","author":"Yuan","year":"2020","journal-title":"Proceedings of the INTERSPEECH 2020"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Altinok, D. (2024). Explainable Multimodal Fusion for Dementia Detection from Text and Speech. Proceedings of the International Conference on Text, Speech, and Dialogue, Springer.","DOI":"10.1007\/978-3-031-70566-3_21"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/TASLPRO.2025.3533363","article-title":"A Two-Step Attention-Based Feature Combination Cross-Attention System for Speech-Based Dementia Detection","volume":"33","author":"Pan","year":"2025","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"4474","DOI":"10.1109\/JBHI.2025.3540207","article-title":"A Dual-Modal Fusion Framework for Detection of Mild Cognitive Impairment Based on Autobiographical Memory","volume":"29","author":"Chang","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_154","unstructured":"Heitz, J., Schneider, G., and Langer, N. (2024). Linguistic Features Extracted by GPT-4 Improve Alzheimer\u2019s Disease Detection Based on Spontaneous Speech. arXiv."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Agbavor, F., and Liang, H. (2022). Predicting Dementia from Spontaneous Speech Using Large Language Models. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000168"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Chen, G., and Jin, Y. (2024). Cascaded Transfer Learning Strategy for Cross-Domain Alzheimer\u2019s Disease Recognition through Spontaneous Speech. Proceedings of the INTERSPEECH 2024, ISCA.","DOI":"10.21437\/Interspeech.2024-627"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Sta\u0161, J., Hl\u00e1dek, D., and Kopnick\u00fd, A. (2024). Detecting Dementia from Transcribed Speech in Slovak Using Pre-Trained BERT Models. Proceedings of the 2024 34th International Conference Radioelektronika (RADIOELEKTRONIKA), IEEE.","DOI":"10.1109\/RADIOELEKTRONIKA61599.2024.10524067"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"104442","DOI":"10.1016\/j.jbi.2023.104442","article-title":"AD-BERT: Using Pre-Trained Language Model to Predict the Progression from Mild Cognitive Impairment to Alzheimer\u2019s Disease","volume":"144","author":"Mao","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Zhang, H., Jethani, N., Jones, S., Genes, N., Major, V.J., Jaffe, I.S., Cardillo, A.B., Heilenbach, N., Ali, N.F., and Bonanni, L.J. (2024). Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores. PLoS Digit. Health, 3.","DOI":"10.1371\/journal.pdig.0000685"},{"key":"ref_160","unstructured":"Shao, M., Xie, Y., Yang, C., and Lu, J. (2026). LLM-MINE: Large Language Model Based Alzheimer\u2019s Disease and Related Dementias Phenotypes Mining from Clinical Notes. arXiv."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Xie, Y., Lu, J., Ho, J., Nahab, F., Hu, X., and Yang, C. (2024). Promptlink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery.","DOI":"10.1145\/3626772.3657904"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1007\/s00371-026-04385-2","article-title":"Large Language Models in Radiogenomics: A Comprehensive Survey of Applications from Imaging to Genetics","volume":"42","author":"Cheema","year":"2026","journal-title":"Vis. Comput."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"113718","DOI":"10.1109\/ACCESS.2025.3582853","article-title":"Multi-Modal Integration Analysis of Alzheimer\u2019s Disease Using Large Language Models and Knowledge Graphs","volume":"13","author":"Kiguchi","year":"2025","journal-title":"IEEE Access"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Lahiri, A.K., and Hu, Q.V. (2025). AlzheimerRAG: Multimodal Retrieval-Augmented Generation for Clinical Use Cases. Mach. Learn. Knowl. Extr., 7.","DOI":"10.3390\/make7030089"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TMI.2025.3594364","article-title":"HoloDx: Knowledge-and Data-Driven Multimodal Diagnosis of Alzheimer\u2019s Disease","volume":"45","author":"Chen","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"102172","DOI":"10.1016\/j.arr.2023.102172","article-title":"Machine Learning in Alzheimer\u2019s Disease Drug Discovery and Target Identification","volume":"93","author":"Geng","year":"2024","journal-title":"Ageing Res. Rev."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Khatami, S.G., Robinson, C., Birkenbihl, C., Domingo-Fern\u00e1ndez, D., Hoyt, C.T., and Hofmann-Apitius, M. (2020). Challenges of Integrative Disease Modeling in Alzheimer\u2019s Disease. Front. Mol. Biosci., 6.","DOI":"10.3389\/fmolb.2019.00158"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"e70014","DOI":"10.1002\/qub2.70014","article-title":"Large Language Models for Bioinformatics","volume":"14","author":"Ruan","year":"2026","journal-title":"Quant. Biol."},{"key":"ref_169","first-page":"e70236","article-title":"Beyond Black-box AI: Interpretable Hybrid Systems for Dementia Care","volume":"18","author":"Kang","year":"2026","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"btaf156","DOI":"10.1093\/bioinformatics\/btaf156","article-title":"Predicting Explainable Dementia Types with LLM-Aided Feature Engineering","volume":"41","author":"Kashyap","year":"2025","journal-title":"Bioinformatics"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1038\/s41591-026-04229-5","article-title":"LLM-Assisted Systematic Review of Large Language Models in Clinical Medicine","volume":"32","author":"Chen","year":"2026","journal-title":"Nat. Med."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Shool, S., Adimi, S., Saboori Amleshi, R., Bitaraf, E., Golpira, R., and Tara, M. (2025). A Systematic Review of Large Language Model (LLM) Evaluations in Clinical Medicine. BMC Med. Inform. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-02954-4"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"100943","DOI":"10.1016\/j.landig.2025.100943","article-title":"CARDBiomedBench: A Benchmark for Evaluating the Performance of Large Language Models in Biomedical Research","volume":"8","author":"Bianchi","year":"2026","journal-title":"Lancet Digit. Health"}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/9\/514\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:35:18Z","timestamp":1789133718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/9\/514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":173,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bios16090514"],"URL":"https:\/\/doi.org\/10.3390\/bios16090514","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}