{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T18:30:45Z","timestamp":1790188245512,"version":"4.1.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,9]]},"DOI":"10.7759\/cureus.75389","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T13:39:38Z","timestamp":1733751578000},"source":"Crossref","is-referenced-by-count":8,"title":["Comparing the Artificial Intelligence Detection Models to Standard Diagnostic Methods and Alternative Models in Identifying Alzheimer\u2019s Disease in At-Risk or Early Symptomatic Individuals: A Scoping Review"],"prefix":"10.7759","author":[{"given":"Britty","family":"Babu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gauri","family":"Parvathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fathima S","family":"Mohideen Bawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gurnoor S","family":"Gill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeeya","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dataar S","family":"Sibia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayadev","family":"Sureddi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vidhi","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-023-01837-4","article-title":"Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study","volume":"23","author":"Aguayo GA","year":"2023","unstructured":"Aguayo GA, Zhang L, Vaillant M, et al.. Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study. BMC Med Res Methodol. 2023, 23:8. 10.1186\/s12874-023-01837-4","journal-title":"BMC Med Res Methodol"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s22082911","article-title":"Brain MRI analysis for Alzheimer's disease diagnosis using CNN-based feature extraction and machine learning","volume":"22","author":"AlSaeed D","year":"2022","unstructured":"AlSaeed D, Omar SF. Brain MRI analysis for Alzheimer's disease diagnosis using CNN-based feature extraction and machine learning. Sensors (Basel). 2022, 22:10.3390\/s22082911","journal-title":"Sensors (Basel)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-18805-5","article-title":"Predicting conversion to Alzheimer's disease in individuals with mild cognitive impairment using clinically transferable features","volume":"12","author":"Rye I","year":"2022","unstructured":"Rye I, Vik A, Kocinski M, Lundervold AS, Lundervold AJ. Predicting conversion to Alzheimer's disease in individuals with mild cognitive impairment using clinically transferable features. Sci Rep. 2022, 12:15566. 10.1038\/s41598-022-18805-5","journal-title":"Sci Rep"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/s21030778","article-title":"Brain asymmetry detection and machine learning classification for diagnosis of early dementia","volume":"21","author":"Herzog NJ","year":"2021","unstructured":"Herzog NJ, Magoulas GD. Brain asymmetry detection and machine learning classification for diagnosis of early dementia. Sensors (Basel). 2021, 21:10.3390\/s21030778","journal-title":"Sensors (Basel)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1111\/acel.13358","article-title":"Platelet biomarkers for a descending cognitive function: a proteomic approach","volume":"20","author":"Yu H","year":"2021","unstructured":"Yu H, Liu Y, He B, et al.. Platelet biomarkers for a descending cognitive function: a proteomic approach. Aging Cell. 2021, 20:e13358. 10.1111\/acel.13358","journal-title":"Aging Cell"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2023.1094233","article-title":"Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?","volume":"15","author":"Mirkin S","year":"2023","unstructured":"Mirkin S, Albensi BC. Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?. Front Aging Neurosci. 2023, 15:1094233. 10.3389\/fnagi.2023.1094233","journal-title":"Front Aging Neurosci"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-021-01626-4","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"10","author":"Page MJ","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, et al.. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021, 10:89. 10.1186\/s13643-021-01626-4","journal-title":"Syst Rev"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcab246","article-title":"Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review","volume":"3","author":"Rowe TW","year":"2021","unstructured":"Rowe TW, Katzourou IK, Stevenson-Hoare JO, Bracher-Smith MR, Ivanov DK, Escott-Price V. Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review. Brain Commun. 2021, 3:fcab246. 10.1093\/braincomms\/fcab246","journal-title":"Brain Commun"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22979-3","article-title":"The development of an automated machine learning pipeline for the detection of Alzheimer's Disease","volume":"12","author":"Chedid N","year":"2022","unstructured":"Chedid N, Tabbal J, Kabbara A, Allouch S, Hassan M. The development of an automated machine learning pipeline for the detection of Alzheimer's Disease. Sci Rep. 2022, 12:18137. 10.1038\/s41598-022-22979-3","journal-title":"Sci Rep"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00169-8","article-title":"A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: a retrospective, multicentre case-control study","volume":"4","author":"Cheung CY","year":"2022","unstructured":"Cheung CY. A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Heal. 2022, 4:806-15. 10.1016\/S2589-7500(22)00169-8","journal-title":"Lancet Digit Heal"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/cells11111744","article-title":"Artificial intelligence and circulating cell-free DNA methylation profiling: mechanism and detection of Alzheimer's disease","volume":"11","author":"Bahado-Singh RO","year":"2022","unstructured":"Bahado-Singh RO, Radhakrishna U, Gordevi\u010dius J, et al.. Artificial intelligence and circulating cell-free DNA methylation profiling: mechanism and detection of Alzheimer's disease. Cells. 2022, 11:10.3390\/cells11111744","journal-title":"Cells"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.81869","article-title":"Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study","volume":"12","author":"Millar PR","year":"2023","unstructured":"Millar PR, Gordon BA, Luckett PH, et al.. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. Elife. 2023, 12:10.7554\/eLife.81869","journal-title":"Elife"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/s22030740","article-title":"An intelligent system for early recognition of Alzheimer's disease using neuroimaging","volume":"22","author":"Odusami M","year":"2022","unstructured":"Odusami M, Maskeli\u016bnas R, Dama\u0161evi\u010dius R. An intelligent system for early recognition of Alzheimer's disease using neuroimaging. Sensors (Basel). 2022, 22:10.3390\/s22030740","journal-title":"Sensors (Basel)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23095237","article-title":"Artificial intelligence predictor for Alzheimer's disease trained on blood transcriptome: the role of oxidative stress","volume":"23","author":"Chiricosta L","year":"2022","unstructured":"Chiricosta L, D'Angiolini S, Gugliandolo A, Mazzon E. Artificial intelligence predictor for Alzheimer's disease trained on blood transcriptome: the role of oxidative stress. Int J Mol Sci. 2022, 23:10.3390\/ijms23095237","journal-title":"Int J Mol Sci"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.834032","article-title":"Evaluation of neuro images for the diagnosis of Alzheimer's disease using deep learning neural network","volume":"10","author":"A A","year":"2022","unstructured":"A A, M P, Hamdi M, Bourouis S, Rastislav K, Mohmed F. Evaluation of neuro images for the diagnosis of Alzheimer's disease using deep learning neural network. Front Public Health. 2022, 10:834032. 10.3389\/fpubh.2022.834032","journal-title":"Front Public Health"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-220585","article-title":"Monitoring Alzheimer's disease progression in mild cognitive impairment stage using machine learning-based FDG-PET classification methods","volume":"89","author":"Beheshti I","year":"2022","unstructured":"Beheshti I, Geddert N, Perron J, Gupta V, Albensi BC, Ko JH. Monitoring Alzheimer's disease progression in mild cognitive impairment stage using machine learning-based FDG-PET classification methods. J Alzheimers Dis. 2022, 89:1493-502. 10.3233\/JAD-220585","journal-title":"J Alzheimers Dis"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/cns.13963","article-title":"Machine learning based on optical coherence tomography images as a diagnostic tool for Alzheimer's disease","volume":"28","author":"Wang X","year":"2022","unstructured":"Wang X, Jiao B, Liu H, et al.. Machine learning based on optical coherence tomography images as a diagnostic tool for Alzheimer's disease. CNS Neurosci Ther. 2022, 28:2206-17. 10.1111\/cns.13963","journal-title":"CNS Neurosci Ther"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119621","article-title":"Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration","volume":"263","author":"Taylor A","year":"2022","unstructured":"Taylor A, Zhang F, Niu X, et al.. Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration. Neuroimage. 2022, 263:119621. 10.1016\/j.neuroimage.2022.119621","journal-title":"Neuroimage"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.853294","article-title":"Early-stage Alzheimer's disease prediction using machine learning models","volume":"10","author":"Kavitha C","year":"2022","unstructured":"Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA. Early-stage Alzheimer's disease prediction using machine learning models. Front Public Health. 2022, 10:853294. 10.3389\/fpubh.2022.853294","journal-title":"Front Public Health"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0276392","article-title":"SPARE-Tau: a flortaucipir machine-learning derived early predictor of cognitive decline","volume":"17","author":"Toledo JB","year":"2022","unstructured":"Toledo JB, Rashid T, Liu H, et al.. SPARE-Tau: a flortaucipir machine-learning derived early predictor of cognitive decline. PLoS One. 2022, 17:e0276392. 10.1371\/journal.pone.0276392","journal-title":"PLoS One"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-26958-6","article-title":"A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm","volume":"12","author":"Elgammal YM","year":"2022","unstructured":"Elgammal YM, Zahran MA, Abdelsalam MM. A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm. Sci Rep. 2022, 12:22381. 10.1038\/s41598-022-26958-6","journal-title":"Sci Rep"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-022-01047-y","article-title":"Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach","volume":"14","author":"Diogo VS","year":"2022","unstructured":"Diogo VS, Ferreira HA, Prata D. Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach. Alzheimers Res Ther. 2022, 14:107. 10.1186\/s13195-022-01047-y","journal-title":"Alzheimers Res Ther"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-27551-1","article-title":"Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction","volume":"13","author":"Gao XR","year":"2023","unstructured":"Gao XR, Chiariglione M, Qin K, Nuytemans K, Scharre DW, Li YJ, Martin ER. Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction. Sci Rep. 2023, 13:450. 10.1038\/s41598-023-27551-1","journal-title":"Sci Rep"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-220021","article-title":"Machine learning based multimodal neuroimaging genomics dementia score for predicting future conversion to Alzheimer's disease","volume":"87","author":"Mirabnahrazam G","year":"2022","unstructured":"Mirabnahrazam G, Ma D, Lee S, et al.. Machine learning based multimodal neuroimaging genomics dementia score for predicting future conversion to Alzheimer's disease. J Alzheimers Dis. 2022, 87:1345-65. 10.3233\/JAD-220021","journal-title":"J Alzheimers Dis"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11011-023-01171-0","article-title":"Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: an explainable artificial intelligence-based approach","volume":"38","author":"Sekaran K","year":"2023","unstructured":"Sekaran K, Alsamman AM, George Priya Doss C, Zayed H. Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: an explainable artificial intelligence-based approach. Metab Brain Dis. 2023, 38:1297-310. 10.1007\/s11011-023-01171-0","journal-title":"Metab Brain Dis"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18632\/aging.203984","article-title":"VEGF-A-related genetic variants protect against Alzheimer's disease","volume":"14","author":"Petrelis AM","year":"2022","unstructured":"Petrelis AM, Stathopoulou MG, Kafyra M, et al.. VEGF-A-related genetic variants protect against Alzheimer's disease. Aging (Albany NY). 2022, 14:2524-36. 10.18632\/aging.203984","journal-title":"Aging (Albany NY)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/genes14061285","article-title":"Metabolic pathway pairwise-based signature as a potential non-invasive diagnostic marker in Alzheimer's disease patients","volume":"14","author":"Feng Y","year":"2023","unstructured":"Feng Y, Chen X, Zhang XD, Huang C. Metabolic pathway pairwise-based signature as a potential non-invasive diagnostic marker in Alzheimer's disease patients. Genes (Basel). 2023, 14:10.3390\/genes14061285","journal-title":"Genes (Basel)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-023-03849-7","article-title":"Functional activity level reported by an informant is an early predictor of Alzheimer's disease","volume":"23","author":"Vik A","year":"2023","unstructured":"Vik A, Koci\u0144ski M, Rye I, Lundervold AJ, Lundervold AS. Functional activity level reported by an informant is an early predictor of Alzheimer's disease. BMC Geriatr. 2023, 23:205. 10.1186\/s12877-023-03849-7","journal-title":"BMC Geriatr"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-215714","article-title":"Automated early detection of Alzheimer's disease by capturing impairments in multiple cognitive domains with multiple drawing tasks","volume":"88","author":"Kobayashi M","year":"2022","unstructured":"Kobayashi M, Yamada Y, Shinkawa K, Nemoto M, Nemoto K, Arai T. Automated early detection of Alzheimer's disease by capturing impairments in multiple cognitive domains with multiple drawing tasks. J Alzheimers Dis. 2022, 88:1075-89. 10.3233\/JAD-215714","journal-title":"J Alzheimers Dis"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/s21227634","article-title":"Diagnosis of Alzheimer\u2019s disease with ensemble learning classifier and 3D convolutional neural network","volume":"21","author":"Zhang P","year":"2021","unstructured":"Zhang P, Lin S, Qiao J, Tu Y. Diagnosis of Alzheimer\u2019s disease with ensemble learning classifier and 3D convolutional neural network. Sensors (Basel). 2021, 21:10.3390\/s21227634","journal-title":"Sensors (Basel)"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-022-00985-x","article-title":"A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease","volume":"14","author":"Feng X","year":"2022","unstructured":"Feng X, Provenzano FA, Small SA. A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease. Alzheimers Res Ther. 2022, 14:45. 10.1186\/s13195-022-00985-x","journal-title":"Alzheimers Res Ther"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106240","article-title":"Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning","volume":"151","author":"Alorf A","year":"2022","unstructured":"Alorf A, Khan MU. Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning. Comput Biol Med. 2022, 151:106240. 10.1016\/j.compbiomed.2022.106240","journal-title":"Comput Biol Med"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26703-z","article-title":"A deep learning framework identifies dimensional representations of Alzheimer's disease from brain structure","volume":"12","author":"Yang Z","year":"2021","unstructured":"Yang Z, Nasrallah IM, Shou H, et al.. A deep learning framework identifies dimensional representations of Alzheimer's disease from brain structure. Nat Commun. 2021, 12:7065. 10.1038\/s41467-021-26703-z","journal-title":"Nat Commun"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3257081","article-title":"Attention-guided autoencoder for automated progression prediction of subjective cognitive decline with structural MRI","volume":"27","author":"Guan H","year":"2023","unstructured":"Guan H, Yue L, Yap PT, Xiao S, Bozoki A, Liu M. Attention-guided autoencoder for automated progression prediction of subjective cognitive decline with structural MRI. IEEE J Biomed Health Inform. 2023, 27:2980-9. 10.1109\/JBHI.2023.3257081","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.1046410","article-title":"Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning","volume":"13","author":"Lai Y","year":"2022","unstructured":"Lai Y, Lin P, Lin F, et al.. Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning. Front Immunol. 2022, 13:1046410. 10.3389\/fimmu.2022.1046410","journal-title":"Front Immunol"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-71358-7","article-title":"Revolutionizing healthcare: a comparative insight into deep learning\u2019s role in medical imaging","volume":"14","author":"Prasad VK","year":"2024","unstructured":"Prasad VK, Verma A, Bhattacharya P, et al.. Revolutionizing healthcare: a comparative insight into deep learning\u2019s role in medical imaging. Sci Rep. 2024, 14:30273. 10.1038\/s41598-024-71358-7","journal-title":"Sci Rep"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-78095-7","article-title":"Cognitive and MRI trajectories for prediction of Alzheimer's disease","volume":"11","author":"Mofrad SA","year":"2021","unstructured":"Mofrad SA, Lundervold AJ, Vik A, Lundervold AS. Cognitive and MRI trajectories for prediction of Alzheimer's disease. Sci Rep. 2021, 11:2122. 10.1038\/s41598-020-78095-7","journal-title":"Sci Rep"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-80312-2","article-title":"Modular machine learning for Alzheimer's disease classification from retinal vasculature","volume":"11","author":"Tian J","year":"2021","unstructured":"Tian J, Smith G, Guo H, et al.. Modular machine learning for Alzheimer's disease classification from retinal vasculature. Sci Rep. 2021, 11:238. 10.1038\/s41598-020-80312-2","journal-title":"Sci Rep"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2021.102712","article-title":"Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease","volume":"31","author":"Bron EE","year":"2021","unstructured":"Bron EE, Klein S, Papma JM, et al.. Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease. Neuroimage Clin. 2021, 31:102712. 10.1016\/j.nicl.2021.102712","journal-title":"Neuroimage Clin"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1186\/s12974-022-02383-4","article-title":"Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer's disease","volume":"19","author":"Mu\u00f1oz-Castro C","year":"2022","unstructured":"Mu\u00f1oz-Castro C, Noori A, Magdamo CG, et al.. Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer's disease. J Neuroinflammation. 2022, 19:30. 10.1186\/s12974-022-02383-4","journal-title":"J Neuroinflammation"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22917-3","article-title":"Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study","volume":"12","author":"Kim JS","year":"2022","unstructured":"Kim JS, Han JW, Bae JB, et al.. Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study. Sci Rep. 2022, 12:18007. 10.1038\/s41598-022-22917-3","journal-title":"Sci Rep"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9442","article-title":"A classification for complex imbalanced data in disease screening and early diagnosis","volume":"41","author":"Li Y","year":"2022","unstructured":"Li Y, Hsu WW. A classification for complex imbalanced data in disease screening and early diagnosis. Stat Med. 2022, 41:3679-95. 10.1002\/sim.9442","journal-title":"Stat Med"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.772592","article-title":"A transfer learning method for detecting alzheimer's disease based on speech and natural language processing","volume":"10","author":"Liu N","year":"2022","unstructured":"Liu N, Luo K, Yuan Z, Chen Y. A transfer learning method for detecting alzheimer's disease based on speech and natural language processing. Front Public Health. 2022, 10:772592. 10.3389\/fpubh.2022.772592","journal-title":"Front Public Health"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/4808841","article-title":"A greedy optimized intelligent framework for early detection of Alzheimer's disease using EEG signal","volume":"2023","author":"Swarnalatha R","year":"2023","unstructured":"Swarnalatha R. A greedy optimized intelligent framework for early detection of Alzheimer's disease using EEG signal. Comput Intell Neurosci. 2023, 2023:4808841. 10.1155\/2023\/4808841","journal-title":"Comput Intell Neurosci"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-10202-2","article-title":"In-depth insights into Alzheimer's disease by using explainable machine learning approach","volume":"12","author":"Bogdanovic B","year":"2022","unstructured":"Bogdanovic B, Eftimov T, Simjanoska M. In-depth insights into Alzheimer's disease by using explainable machine learning approach. Sci Rep. 2022, 12:6508. 10.1038\/s41598-022-10202-2","journal-title":"Sci Rep"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/320264-comparing-the-artificial-intelligence-detection-models-to-standard-diagnostic-methods-and-alternative-models-in-identifying-alzheimers-disease-in-at-risk-or-early-symptomatic-individuals-a-scoping-review","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T13:39:41Z","timestamp":1733751581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/320264-comparing-the-artificial-intelligence-detection-models-to-standard-diagnostic-methods-and-alternative-models-in-identifying-alzheimers-disease-in-at-risk-or-early-symptomatic-individuals-a-scoping-review"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":45,"URL":"https:\/\/doi.org\/10.7759\/cureus.75389","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12,9]]}}}