{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T17:09:50Z","timestamp":1789837790496,"version":"4.0.1"},"reference-count":71,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Biomedical Sensors and Systems Lab, University of North Florida, Jacksonville, FL 32224"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Alzheimer\u2019s disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer\u2019s disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer\u2019s disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer\u2019s disease detection and highlights future research directions.<\/jats:p>","DOI":"10.3390\/diagnostics14161759","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T06:02:37Z","timestamp":1723528957000},"page":"1759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Early Alzheimer\u2019s Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0988-7973","authenticated-orcid":false,"given":"Soraisam Gobinkumar","family":"Singh","sequence":"first","affiliation":[{"name":"Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9211-2314","authenticated-orcid":false,"given":"Dulumani","family":"Das","sequence":"additional","affiliation":[{"name":"Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Utpal","family":"Barman","sequence":"additional","affiliation":[{"name":"Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-4333","authenticated-orcid":false,"given":"Manob Jyoti","family":"Saikia","sequence":"additional","affiliation":[{"name":"Biomedical Sensors and Systems Lab, University of North Florida, Jacksonville, FL 32224, USA"},{"name":"Department of Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s41572-021-00269-y","article-title":"Alzheimer disease","volume":"7","author":"Knopman","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","unstructured":"(2023, November 05). Available online: https:\/\/www.alzint.org\/resource\/world-alzheimer-report-2023."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1002\/alz.12928","article-title":"Prevalence of dementia in india: National and state estimates from a nationwide study","volume":"19","author":"Lee","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.neuroimage.2008.03.050","article-title":"Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI","volume":"41","author":"Davatzikos","year":"2008","journal-title":"Neuroimage"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S281","DOI":"10.1046\/j.1532-5415.5153.x","article-title":"Early diagnosis of alzheimer\u2019s disease: Clinical and economic benefits","volume":"51","author":"Leifer","year":"2003","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1016\/j.matpr.2020.07.645","article-title":"Detection and analysis of alzheimer\u2019s disease using various machine learning algorithms","volume":"45","author":"Kishore","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/jon.13063","article-title":"Functional magnetic resonance imaging, deep learning, and alzheimer\u2019s disease: A systematic review","volume":"33","author":"Warren","year":"2023","journal-title":"J. Neuroimaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Flanagan, K., and Saikia, M.J. (2023). Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing. Sensors, 23.","DOI":"10.3390\/s23208482"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/1094428118802631","article-title":"An overview of functional magnetic resonance imaging techniques for organizational research","volume":"22","author":"Preuschoff","year":"2019","journal-title":"Organ. Res. Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2981","DOI":"10.1002\/hbm.26256","article-title":"A spectral sampling algorithm in dynamic causal modelling for resting-state fMRI","volume":"44","author":"Xie","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yue, J.-H., Zhang, Q.-H., Yang, X., Wang, P., Sun, X.-C., Yan, S.-Y., Li, A., Cao, D.-N., Wang, Y., and Wei, Z.-Y. (2023). Magnetic resonance imaging of white matter in alzheimer\u2019s disease: A global bibliometric analysis from 1990 to 2022. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1163809"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"a006213","DOI":"10.1101\/cshperspect.a006213","article-title":"Brain imaging in alzheimer disease","volume":"2","author":"Johnson","year":"2012","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.jalz.2015.05.002","article-title":"Magnetic resonance imaging in alzheimer\u2019s disease neuroimaging initiative 2","volume":"11","author":"Jack","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_14","first-page":"e1","article-title":"Recent publications from the alzheimer\u2019s disease neuroimaging initiative: Reviewing progress toward improved AD clinical trials","volume":"13","author":"Weiner","year":"2017","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_15","first-page":"685","article-title":"The alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1017\/S1041610209009405","article-title":"The australian imaging, biomarkers and lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of alzheimer\u2019s disease","volume":"21","author":"Ellis","year":"2009","journal-title":"Int. Psychogeriatr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., and Vlassenko, A.G. (2019). OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv.","DOI":"10.1101\/2019.12.13.19014902"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neuroimage.2012.12.044","article-title":"Miriad\u2014Public release of a multiple time point alzheimer\u2019s MR imaging dataset","volume":"70","author":"Malone","year":"2013","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3233\/JAD-160621","article-title":"Sample size estimation for alzheimer\u2019s disease trials from Japanese adni serial magnetic resonance imaging","volume":"56","author":"Fujishima","year":"2017","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.jalz.2010.03.011","article-title":"Japanese alzheimer\u2019s disease neuroimaging initiative: Present status and future","volume":"6","author":"Iwatsubo","year":"2010","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Diaz, V., and Rodr\u00edguez, G.H. (2022). Machine learning for detection of cognitive impairment. Acta Polytech. Hung., 19.","DOI":"10.12700\/APH.19.5.2022.5.10"},{"key":"ref_22","first-page":"519","article-title":"Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review","volume":"10","author":"Pellegrini","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11065-007-9032-z","article-title":"Early detection of memory impairment in alzheimer\u2019s disease: A neurocognitive perspective on assessment","volume":"17","author":"Lowndes","year":"2007","journal-title":"Neuropsychol. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1097\/NEN.0b013e31824b211b","article-title":"Accuracy of the clinical diagnosis of alzheimer disease at national institute on aging Alzheimer disease centers, 2005\u20132010","volume":"71","author":"Beach","year":"2012","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1093\/oxfordjournals.aje.a009864","article-title":"Smoking and the occurence of alzheimer\u2019s disease: Cross-sectional and longitudinal data in a population-based study","volume":"149","author":"Wang","year":"1999","journal-title":"Am. J. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.jval.2019.02.012","article-title":"Machine learning for health services researchers","volume":"22","author":"Doupe","year":"2019","journal-title":"Value Health"},{"key":"ref_27","first-page":"458","article-title":"Exploring the applications of machine learning in healthcare","volume":"10","author":"Saleem","year":"2020","journal-title":"Int. J. Sens. Wirel. Commun. Control"},{"key":"ref_28","first-page":"1","article-title":"Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: A review","volume":"16","author":"Tanveer","year":"2020","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_29","first-page":"86","article-title":"Survey on neural networks used for medical image processing","volume":"3","author":"Shi","year":"2009","journal-title":"Int. J. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1016\/j.neuroimage.2012.01.055","article-title":"Ensemble sparse classification of alzheimer\u2019s disease","volume":"60","author":"Liu","year":"2012","journal-title":"NeuroImage"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"An, N., Ding, H., Yang, J., Au, R., and Ang, T.F. (2020). Deep ensemble learning for alzheimer\u2019s disease classification. J. Biomed. Inform., 105.","DOI":"10.1016\/j.jbi.2020.103411"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","article-title":"Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"NeuroImage"},{"key":"ref_34","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101929","DOI":"10.1016\/j.nicl.2019.101929","article-title":"Cortical graph neural network for ad and mci diagnosis and transfer learning across populations","volume":"23","author":"Wee","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/jrsm.1535","article-title":"Introduction to prisma 2020 and implications for research synthesis methodologists","volume":"13","author":"Page","year":"2022","journal-title":"Res. Synth. Methods"},{"key":"ref_37","first-page":"206","article-title":"Multimodal prediction of conversion to alzheimer\u2019s disease based on incomplete biomarkers","volume":"1","author":"Ritter","year":"2015","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s13195-017-0301-7","article-title":"Incremental value of biomarker combinations to predict progression of mild cognitive impairment to alzheimer\u2019s dementia","volume":"9","author":"Peters","year":"2017","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Long, X., Chen, L., Jiang, C., Zhang, L., and Alzheimer\u2019s Disease Neuroimaging Initiative (2017). Prediction and classification of alzheimer disease based on quantification of mri deformation. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0173372"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pereira, T., Lemos, L., Cardoso, S., Silva, D., Rodrigues, A., Santana, I., de Mendon\u00e7a, A., Guerreiro, M., and Madeira, S.C. (2017). Predicting progression of mild cognitive impairment to dementia using neuropsychological data: A supervised learning approach usingtime windows. BMC Med. Inform. Decis. Mak., 17.","DOI":"10.1186\/s12911-017-0497-2"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yao, Z., Zheng, W., Yang, J., Ding, Z., Li, M., and Lu, S. (2017, January 13\u201316). Predicting MCI progression with individual metabolic network based on longitudinal FDG-PET. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA.","DOI":"10.1109\/BIBM.2017.8217948"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sun, Z., Van de Giessen, M., Lelieveldt, B.P., and Staring, M. (2017). Detection of conversion from mild cognitive impairment to alzheimer\u2019s disease using longitudinal brain mri. Front. Neuroinform., 11.","DOI":"10.3389\/fninf.2017.00016"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gavidia-Bovadilla, G., Kanaan-Izquierdo, S., Matar\u00f3-Serrat, M., Perera-Lluna, A., and Alzheimer\u2019s Disease Neuroimaging Initiative (2017). Early prediction of alzheimer\u2019s disease using null longitudinal model-based classifiers. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0168011"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.mri.2018.03.003","article-title":"Comparison of feature representations in MRI-based MCI-to-AD conversion prediction","volume":"50","author":"Tohka","year":"2018","journal-title":"Magn. Reson. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shen, T., Jiang, J., Li, Y., Wu, P., Zuo, C., and Yan, Z. (2018, January 18\u201321). Decision supporting model for one-year conversion probability from MCI to AD using CNN and SVM. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512398"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compbiomed.2018.09.004","article-title":"Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI","volume":"102","author":"Hojjati","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"115549","DOI":"10.1016\/j.eswa.2021.115549","article-title":"Data fusion based on search light analysis for the prediction of alzheimer\u2019s disease","volume":"185","author":"Arco","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102712","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\u2019s disease","volume":"31","author":"Bron","year":"2021","journal-title":"NeuroImage Clin."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1002\/alz.12645","article-title":"Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis","volume":"18","author":"Rossini","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s13042-022-01570-2","article-title":"A novelty detection approach to effectively predict conversion from mild cognitive impairment to alzheimer\u2019s disease","volume":"14","author":"Liu","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, K., Chen, K., Yao, L., and Guo, X. (2017). Prediction of mild cognitive impairment conversion using a combination of independent component analysis and the cox model. Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00033"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kauppi, K., Fan, C.C., McEvoy, L.K., Holland, D., Tan, C.H., Chen, C.H., Andreassen, O.A., Desikan, R.S., Dale, A.M., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Combining polygenic hazard score with volumetric mri and cognitive measures improves prediction of progression from mild cognitive impairment to alzheimer\u2019s disease. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00260"},{"key":"ref_53","first-page":"887","article-title":"Identification of alzheimer\u2019s disease and mild cognitive impairment using networks constructed based on multiple morphological brain features","volume":"3","author":"Zheng","year":"2018","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1038\/s43856-022-00133-4","article-title":"A predictive model using the mesoscopic architecture of the living brain to detect alzheimer\u2019s disease","volume":"2","author":"Inglese","year":"2022","journal-title":"Commun. Med."},{"key":"ref_55","first-page":"755","article-title":"Alzheimer\u2019s disease: 3-dimensional MRI texture for prediction of conversion from mild cognitive impairment","volume":"10","author":"Luk","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neunet.2023.04.018","article-title":"Prospective classification of alzheimer\u2019s disease conversion from mild cognitive impairment","volume":"164","author":"Park","year":"2023","journal-title":"Neural Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s12021-016-9318-5","article-title":"Multi-domain transfer learning for early diagnosis of alzheimer\u2019s disease","volume":"15","author":"Cheng","year":"2017","journal-title":"Neuroinformatics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Multimodal and multiscale deep neural networks for the early diagnosis of alzheimer\u2019s disease using structural MR and FDG-PET images. Sci. Rep., 8.","DOI":"10.1016\/j.media.2018.02.002"},{"key":"ref_59","unstructured":"Lee, G., Nho, K., Kang, B., Sohn, K.-A., and Kim, D. (2019). Predicting alzheimer\u2019s disease progression using multi-modal deep learning approach. Sci. Rep., 9."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., and Qu, X. (2018). Convolutional neural networks-based mri image analysis for the alzheimer\u2019s disease prediction from mild cognitive impairment. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00777"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks","volume":"21","author":"Basaia","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"102290","DOI":"10.1016\/j.nicl.2020.102290","article-title":"AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction","volume":"27","author":"Gao","year":"2020","journal-title":"NeuroImage Clin."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","article-title":"Landmark-based deep multi-instance learning for brain disease diagnosis","volume":"43","author":"Liu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.neuroimage.2018.08.040","article-title":"Using high-dimensional machine learning methods to estimate an anatomical risk factor for alzheimer\u2019s disease across imaging databases","volume":"183","author":"Casanova","year":"2018","journal-title":"Neuroimage"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wei, Y., Price, S.J., Sch\u00f6nlieb, C.-B., and Li, C. (2022). Predicting conversion of mild cognitive impairment to alzheimer\u2019s disease. arXiv.","DOI":"10.1109\/ISBI53787.2023.10230694"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"14487","DOI":"10.1007\/s00521-022-07263-9","article-title":"Two-stage deep learning model for alzheimer\u2019s disease detection and prediction of the mild cognitive impairment time","volume":"34","author":"Saleh","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lu, P., Hu, L., Zhang, N., Liang, H., Tian, T., and Lu, L. (2022). A two-stage model for predicting mild cognitive impairment to alzheimer\u2019s disease conversion. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.826622"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1007\/s11227-022-04668-0","article-title":"MRI-based model for mci conversion using deep zero-shot transfer learning","volume":"79","author":"Ren","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","article-title":"The alzheimer\u2019s disease neuroimaging initiative","volume":"15","author":"Mueller","year":"2005","journal-title":"Neuroimaging Clin."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, M., Cheng, D., Yan, W., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Classification of alzheimer\u2019s disease by combination of convolutional and recurrent neural networks using fdg-pet images. Front. Neuroinformatics, 12.","DOI":"10.3389\/fninf.2018.00035"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kim, N., Borthakur, D., and Saikia, M.J. (2024, January 15\u201324). Examining Brainwave Patterns in Response to Familiar Music: An EEG and Machine Learning Approach. Proceedings of the IEEE SoutheastCon, The Westin Peachtree Plaza, Atlanta, GA, USA.","DOI":"10.1109\/SoutheastCon52093.2024.10500273"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/16\/1759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:35:47Z","timestamp":1760110547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/16\/1759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":71,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["diagnostics14161759"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14161759","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]}}}