{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T03:03:51Z","timestamp":1792551831925,"version":"4.5.0"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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><jats:title>Background<\/jats:title><jats:p>Alzheimer\u2019s disease and mild cognitive impairment are often difficult to differentiate due to their progressive nature and overlapping symptoms. The lack of reliable biomarkers further complicates early diagnosis. As the global population ages, the incidence of cognitive disorders increases, making the need for accurate diagnosis critical. Timely and precise diagnosis is essential for the effective treatment and intervention of these conditions. However, existing diagnostic methods frequently lead to a significant rate of misdiagnosis. This issue underscores the necessity for improved diagnostic techniques to better identify cognitive disorders in the aging population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used Graph Neural Networks, Multi-Layer Perceptrons, and Graph Attention Networks. GNNs map patient data into a graph structure, with nodes representing patients and edges shared clinical features, capturing key relationships. MLPs and GATs are used to analyse discrete data points for tasks such as classification and regression. Each model was evaluated on accuracy, precision, and recall.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The AI models provide an objective basis for comparing patient data with reference populations. This approach enhances the ability to accurately distinguish between AD and MCI, offering more precise risk stratification and aiding in the development of personalized treatment strategies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The incorporation of AI methodologies such as GNNs and MLPs into clinical settings holds promise for enhancing the diagnosis and management of Alzheimer\u2019s disease and mild cognitive impairment. By deploying these advanced computational techniques, clinicians could see a reduction in diagnostic errors, facilitating earlier, more precise interventions, and likely to lead to significantly improved outcomes for patients.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2024.1488050","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T07:09:27Z","timestamp":1732864167000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Advanced AI techniques for classifying Alzheimer\u2019s disease and mild cognitive impairment"],"prefix":"10.3389","volume":"16","author":[{"given":"Sophie","family":"Tascedda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierfrancesco","family":"Sarti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica","family":"Rivi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia Savia","family":"Guerrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe Alessio","family":"Platania","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Santagati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Caraci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna M. C.","family":"Blom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimers Dement. J. Alzheimers Assoc."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e0303644","DOI":"10.1371\/journal.pone.0303644","article-title":"Machine learning for the detection and diagnosis of cognitive impairment in Parkinson\u2019s disease: a systematic review","volume":"19","author":"Altham","year":"2024","journal-title":"PLoS One"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"706095","DOI":"10.3389\/fnagi.2021.706095","article-title":"Artificial intelligence for early diagnosis of Alzheimer\u2019s disease: current state and future directions","volume":"13","author":"Amin","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"5262","DOI":"10.1002\/alz.13886","article-title":"Prediction of Alzheimer\u2019s disease progression within 6 years using speech: a novel approach leveraging language models","volume":"20","author":"Amini","year":"2024","journal-title":"Alzheimers Dement."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1016\/j.artmed.2013.04.002","article-title":"Predicting Alzheimer\u2019s disease with a wrapper-based feature selection method and support vector machines","volume":"34","author":"Armananzas","year":"2013","journal-title":"Pattern Recogn. Lett."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40708-023-00195-7","article-title":"A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer\u2019s disease","volume":"10","author":"Arya","year":"2023","journal-title":"Brain Inform."},{"key":"ref7","first-page":"1","article-title":"An accurate prediction and diagnosis of Alzheimer\u2019s disease using deep learning","author":"Baskar","year":"2023"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s12021-019-09495-3","article-title":"Machine learning for early detection of Alzheimer\u2019s disease: a systematic review","volume":"17","author":"Bertozzi","year":"2019","journal-title":"Neuroinformatics"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.neuroimage.2015.01.048","article-title":"Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge","volume":"111","author":"Bron","year":"2015","journal-title":"NeuroImage"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fninf.2020.00025","article-title":"A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features","volume":"14","author":"Castellazzi","year":"2020","journal-title":"Front. Neuroinform."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1271680","DOI":"10.3389\/frdem.2023.1271680","article-title":"Prediction of progression from mild cognitive impairment to Alzheimer\u2019s disease with longitudinal and multimodal data","volume":"2","author":"Ding","year":"2023","journal-title":"Front. Dement."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/S1474-4422(14)70090-0","article-title":"Advancing research diagnostic criteria for Alzheimer\u2019s disease: the IWG-2 criteria","volume":"13","author":"Dubois","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/bioengineering10050531","article-title":"CNN-based identification of Parkinson\u2019s disease from continuous speech in Noisy environments","volume":"10","author":"Farag\u00f3","year":"2023","journal-title":"Bioengineering"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1199736","DOI":"10.3389\/fncom.2023.1199736","article-title":"Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS","volume":"17","author":"Geraci","year":"2024","journal-title":"Front. Comput. Neurosci."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e49147","DOI":"10.2196\/49147","article-title":"A stable and scalable digital composite neurocognitive test for early dementia screening based on machine learning: model development and validation study","volume":"25","author":"Gu","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref16","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"244","DOI":"10.4236\/jbise.2021.144020","article-title":"A review of machine learning algorithms in the detection of Alzheimer\u2019s disease","volume":"14","author":"Hsu","year":"2021","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1038\/s41591-024-02988-7","article-title":"Revised criteria for the diagnosis and staging of Alzheimer\u2019s disease","volume":"30","author":"Jack","year":"2024","journal-title":"Nat. Med."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA research framework: toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimers Dement."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2020.06.002","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Jiang","year":"2020","journal-title":"AI Open"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCBB.2017.2776910","article-title":"Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning","volume":"16","author":"Ju","year":"2017","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref23","volume-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2017"},{"key":"ref24","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"9235","DOI":"10.5194\/acp-16-9235-2016","article-title":"Theoretical study of mixing in liquid clouds \u2013 part 1: classical concepts","volume":"16","author":"Korolev","year":"2016","journal-title":"Atmospheric Chem. Phys."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.1016\/j.jacc.2020.10.028","article-title":"Artificial intelligence in cardiovascular imaging: current applications and future directions","volume":"76","author":"Kumar","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1109\/RBME.2018.2796598","article-title":"Quantitative MRI brain studies in mild cognitive impairment and Alzheimer\u2019s disease: a methodological review","volume":"13","author":"Leandrou","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s12152-023-09522-5","article-title":"Mild cognitive impairment in relation to Alzheimer\u2019s disease: an investigation of principles, classifications, ethics, and problems","volume":"16","author":"Lee","year":"2023","journal-title":"Neuroethics"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1093\/schbul\/sbac047","article-title":"Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia","volume":"48","author":"Lei","year":"2022","journal-title":"Schizophr. Bull."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1109\/JBHI.2015.2429556","article-title":"A robust deep model for improved classification of AD\/MCI patients","volume":"23","author":"Li","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10462-019-09783-2","article-title":"The integration of deep learning and graph theory for predicting Alzheimer\u2019s disease","volume":"53","author":"Li","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","article-title":"Hierarchical fully convolutional network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI","volume":"42","author":"Lian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TBME.2020.3038634","article-title":"Application of graph neural networks in Alzheimer\u2019s disease classification","volume":"68","author":"Liu","year":"2021","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"104165","DOI":"10.1016\/j.compbiomed.2020.104165","article-title":"A survey on deep learning techniques for Alzheimer\u2019s disease diagnosis and prediction","volume":"129","author":"Ma","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2304.07288","author":"Mao","year":"2023","journal-title":"Cross-entropy loss functions: theoretical analysis and applications"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","article-title":"The diagnosis of dementia due to Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"McKhann","year":"2011","journal-title":"Alzheimers Dement. J. Alzheimers Assoc."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s44163-023-00049-5","article-title":"Evaluation of artificial intelligence techniques in disease diagnosis and prediction","volume":"3","author":"Nia","year":"2023","journal-title":"Discov. Artif. Intell."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1270688","DOI":"10.3389\/fneur.2024.1270688","article-title":"Identifying clinical phenotypes of frontotemporal dementia in post-9\/11 era veterans using natural language processing","volume":"15","author":"Panahi","year":"2024","journal-title":"Front. Neurol."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.008","article-title":"Disease prediction using graph convolutional networks: application to autism Spectrum disorder and Alzheimer\u2019s disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"Pinto-Coelho","year":"2023","journal-title":"Bioengineering"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ymeth.2021.01.004","article-title":"Prediction and interpretation of cancer survival using graph convolution neural networks","volume":"192","author":"Ramirez","year":"2021","journal-title":"Methods San Diego Calif"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","article-title":"A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer\u2019s disease and its prodromal stages","volume":"155","author":"Rathore","year":"2017","journal-title":"NeuroImage"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"3070","DOI":"10.1093\/brain\/awae196","article-title":"Artificial intelligence classifies primary progressive aphasia from connected speech","volume":"147","author":"Rezaii","year":"2024","journal-title":"Brain"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"e12554","DOI":"10.1002\/dad2.12554","article-title":"Diagnosing Alzheimer\u2019s disease: which dementia screening test to use in elderly Puerto Ricans with mild cognitive impairment and early Alzheimer\u2019s disease?","volume":"16","author":"Rodr\u00edguez-Santiago","year":"2024","journal-title":"Alzheimers Dement. Diagn. Assess. Dis. Monit."},{"key":"ref45","first-page":"87","article-title":"Evidence based emergency medicine part 2: positive and negative predictive values of diagnostic tests","volume":"3","author":"Safari","year":"2015","journal-title":"Emergency"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.08631","author":"Sarraf","year":"2016","journal-title":"Classification of Alzheimer\u2019s disease using fMRI data and deep learning convolutional neural networks"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1002\/hbm.23091","article-title":"Early detection of Alzheimer\u2019s disease using MRI hippocampal texture","volume":"37","author":"S\u00f8rensen","year":"2016","journal-title":"Hum. Brain Mapp."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","article-title":"A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease","volume":"189","author":"Spasov","year":"2019","journal-title":"NeuroImage"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.jalz.2011.03.003","article-title":"Towards defining the preclinical stages of Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Sperling","year":"2011","journal-title":"Alzheimers Dement."},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","author":"Vaswani","year":"2023","journal-title":"Attention is all you need"},{"key":"ref52","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"2024.08.29.24312807","DOI":"10.1101\/2024.08.29.24312807","article-title":"Digital language markers distinguish frontal from right anterior temporal lobe atrophy in frontotemporal dementia","author":"Vonk","year":"2024","journal-title":"medRxiv"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"e12367","DOI":"10.1002\/dad2.12367","article-title":"Evaluation of dose-dependent treatment effects after mid-trial dose escalation in biomarker, clinical, and cognitive outcomes for gantenerumab or solanezumab in dominantly inherited Alzheimer\u2019s disease","volume":"14","author":"Wang","year":"2022","journal-title":"Alzheimers Dement. Amst. Neth."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"101785","DOI":"10.1016\/j.media.2020.101785","article-title":"Deep learning for medical image analysis: a comprehensive review","volume":"65","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref56","first-page":"85","article-title":"Hierarchical structure based on deep convolutional neural network for brain tumor classification","volume":"12","author":"Wang","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"ref57","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref58","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.future.2020.12.026","article-title":"Graph-CAT: graph co-attention networks via local and global attribute augmentations","volume":"118","author":"Yang","year":"2021","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/s11065-020-09477-2","article-title":"A review on applications of machine learning algorithms in Alzheimer\u2019s disease","volume":"30","author":"Yao","year":"2020","journal-title":"Neuropsychol. Rev."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.ajp.2019.08.005","article-title":"Network analysis of schizotypal personality traits and their association with other subclinical psychiatric features","volume":"44","author":"Zhang","year":"2019","journal-title":"Asian J. Psychiatry"},{"key":"ref61","doi-asserted-by":"publisher","first-page":"610","DOI":"10.2174\/1567205018666210312112283","article-title":"Alzheimer\u2019s disease diagnosis with deep learning: a review","volume":"18","author":"Zhang","year":"2021","journal-title":"Curr. Alzheimer Res."},{"key":"ref62","first-page":"58","article-title":"A hierarchical feature extraction model for Alzheimer\u2019s disease detection based on MR images","volume":"348","author":"Zhao","year":"2019","journal-title":"Neurocomputing"}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2024.1488050\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T07:09:31Z","timestamp":1732864171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2024.1488050\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":62,"alternative-id":["10.3389\/fnagi.2024.1488050"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2024.1488050","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"article-number":"1488050"}}