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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Review Article

A Review on the Use of Modern Computational Methods in Alzheimer’s Disease-Detection and Prediction

Author(s): Arka De, Tusar Kanti Mishra*, Sameeksha Saraf, Balakrushna Tripathy and Shiva Shankar Reddy

Volume 20, Issue 12, 2023

Published on: 08 March, 2024

Page: [845 - 861] Pages: 17

DOI: 10.2174/0115672050301514240307071217

Abstract

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer’s disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer’s disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

Keywords: Alzheimer's disease, machine learning, computational tools, prediction, multidisciplinary research, cutting-edge solutions.

« Previous Next »
[1]
Hippius, H.; Neundorfer, G. The discovery of alzheimer’s disease. Dialogues Clin. Neurosci., 2022, 5(1), 101-108.
[PMID: 22034141]
[2]
Fargo, K.; Bleiler, L. Alzheimer’s association report: 2014 alzheimers disease facts and figures. Alzheimers Dement., 2014, 10(2), e47-e92.
[PMID: 24818261]
[3]
Korczyn, A.D.; Vakhapova, V. The prevention of the dementia epidemic. J. Neurol. Sci., 2007, 257(1-2), 2-4.
[http://dx.doi.org/10.1016/j.jns.2007.01.081] [PMID: 17490685]
[4]
Tanzi, R.E.; Bertram, L. New frontiers in Alzheimer’s disease genetics. Neuron, 2001, 32(2), 181-184.
[http://dx.doi.org/10.1016/S0896-6273(01)00476-7] [PMID: 11683989]
[5]
Weintraub, S.; Wicklund, A.H.; Salmon, D.P. The neuropsychological profile of Alzheimer disease. Cold Spring Harb. Perspect. Med., 2012, 2(4), a006171.
[http://dx.doi.org/10.1101/cshperspect.a006171] [PMID: 22474609]
[6]
Buzug, T.M. Computed tomography; Springer, 2011.
[7]
Johnson, K.A.; Fox, N.C.; Sperling, R.A.; Klunk, W.E. Brain imaging in Alzheimer disease. Cold Spring Harb. Perspect. Med., 2012, 2(4), a006213.
[http://dx.doi.org/10.1101/cshperspect.a006213] [PMID: 22474610]
[8]
O’Brien, J.T.; Firbank, M.J.; Davison, C.; Barnett, N.; Bamford, C.; Donaldson, C.; Olsen, K.; Herholz, K.; Williams, D.; Lloyd, J. 18F-FDG PET and perfusion SPECT in the diagnosis of Alzheimer and Lewy body dementias. J. Nucl. Med., 2014, 55(12), 1959-1965.
[http://dx.doi.org/10.2967/jnumed.114.143347] [PMID: 25453043]
[9]
Hansson, O.; Zetterberg, H.; Buchhave, P.; Londos, E.; Blennow, K.; Minthon, L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurol., 2006, 5(3), 228-234.
[http://dx.doi.org/10.1016/S1474-4422(06)70355-6] [PMID: 16488378]
[10]
Petersen, RC.; Aisen, PS.; Beckett, LA. Alzheimer's disease neuroimaging initiative (ADNI): Clinical characterization. Neurology, 2010, 74(3), 201-209.
[http://dx.doi.org/10.1212/WNL.0b013e3181cb3e25]
[11]
Marcus, DS.; Fotenos, A.F.; Csernansky, J.G.; Morris, J.C.; Buckner, R.L. Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci., 2010, 22(12), 2677-2684.
[http://dx.doi.org/10.1162/jocn.2009.21407]
[12]
Bendlin, B.; Li, S.J. Alzheimer’s disease connectome project. Connectome coordination facility 2020. Available from: https://www.humanconnectome.org/study/alzheimers-disease-connectome-project
[13]
Armstrong, R.A. Plaques and tangles and the pathogenesis of Alzheimer’s disease. Folia Neuropathol., 2006, 44(1), 1-11.
[PMID: 16565925]
[14]
Ling, Y.; Morgan, K.; Kalsheker, N. Amyloid precursor protein (APP) and the biology of proteolytic processing: Relevance to alzheimer’s disease. Int. J. Biochem. Cell Biol., 2003, 35(11), 1505-1535.
[http://dx.doi.org/10.1016/S1357-2725(03)00133-X] [PMID: 12824062]
[15]
Gouras, G.K.; Olsson, T.T.; Hansson, O. β-Amyloid peptides and amyloid plaques in alzheimer’s disease. Neurotherapeutics, 2015, 12(1), 3-11.
[http://dx.doi.org/10.1007/s13311-014-0313-y] [PMID: 25371168]
[16]
Kolarova, M.; Garc’ıa-Sierra, F.; Bartos, A.; Ricny, J.; Ripova, D. Structure and pathology of tau protein in Alzheimer disease. In: Int J Alzheimers Dis. ; , 2012; 2012, .
[http://dx.doi.org/10.1155/2012/731526]
[17]
Zempel, H.; Mandelkow, E. Lost after translation: Missorting of Tau protein and consequences for Alzheimer disease. Trends Neurosci., 2014, 37(12), 721-732.
[http://dx.doi.org/10.1016/j.tins.2014.08.004] [PMID: 25223701]
[18]
Rossor, M.N.; Fox, N.C.; Freeborough, P.A.; Harvey, R.J. Clinical features of sporadic and familial Alzheimer’s disease. Neurodegeneration, 1996, 5(4), 393-397.
[http://dx.doi.org/10.1006/neur.1996.0052] [PMID: 9117552]
[19]
Kim , J.; Basak , JM.; Holtzman , D.M. The role of apolipoprotein E in Alzheimer’s disease. Neuron, 1996, 63(3), 287-303.
[20]
Hou, Y.; Song, H.; Croteau, D.L.; Akbari, M.; Bohr, V.A. Genome instability in Alzheimer disease. Mech. Ageing Dev., 2017, 161(Pt A), 83-94.
[http://dx.doi.org/10.1016/j.mad.2016.04.005] [PMID: 27105872]
[21]
Potter, H.; Granic, A.; Caneus, J. Role of trisomy 21 mosaicism in sporadic and familial alzheimer’s disease. Curr. Alzheimer Res., 2015, 13(1), 7-17.
[http://dx.doi.org/10.2174/156720501301151207100616] [PMID: 26651340]
[22]
Scheltens, P.; De Strooper, B.; Kivipelto, M.; Holstege, H.; Chételat, G.; Teunissen, C.E.; Cummings, J.; van der Flier, W.M. Alzheimer’s disease. Lancet, 2021, 397(10284), 1577-1590.
[http://dx.doi.org/10.1016/S0140-6736(20)32205-4] [PMID: 33667416]
[23]
Fish, P.V.; Steadman, D.; Bayle, E.D.; Whiting, P. New approaches for the treatment of Alzheimer’s disease. Bioorg. Med. Chem. Lett., 2019, 29(2), 125-133.
[http://dx.doi.org/10.1016/j.bmcl.2018.11.034] [PMID: 30501965]
[24]
Vaz, M.; Silvestre, S. Alzheimer’s disease: Recent treatment strategies. Eur. J. Pharmacol., 2020, 887, 173554.
[http://dx.doi.org/10.1016/j.ejphar.2020.173554] [PMID: 32941929]
[25]
Russell, S.J. Artificial intelligence a modern approach; Pearson Education, Inc., 2010.
[26]
McCarthy, J. What is artificial intelligence, 2007.
[27]
Machinery, C. Computing machinery and intelligence am turing. Mind, 1950, 59(236), 433.
[28]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. In: Nature; , 2015; 521, pp. 436-444.
[29]
Shankle, W.R.; Mani, S.; Pazzani, M.J.; Smyth, P. Detecting very early stages of dementia from normal aging with machine learning methods. Artificial Intelligence in Medicine: 6th Conference on Artificial Intelligence in Medicine Europe, AIME’97 Grenoble, France, March 23–26, 1997 Proceedings 6, pp. 71–85, Springer, 1997.
[http://dx.doi.org/10.1007/BFb0029438]
[30]
Zhang, D.; Wang, Y.; Zhou, L.; Yuan, H.; Shen, D.; Initiative, A.D.N. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage, 2011, 55(3), 856-867.
[http://dx.doi.org/10.1016/j.neuroimage.2011.01.008] [PMID: 21236349]
[31]
Shang, Q.; Zhang, Q.; Liu, X.; Zhu, L. Prediction of early Alzheimer disease by hippocampal volume changes under machine learning algorithm; Computational and Mathematical Methods in Medicine, 2022, 2022, .
[32]
Aderghal, K.; Khvostikov, A.; Krylov, A.; Benois-Pineau, J.; Afdel, K.; Catheline, G. Classification of Alzheimer disease on imaging modalities with deep cnns using cross-modal transfer learning. 2018 IEEE 31st international symposium on computer-based medical systems (CBMS), Karlstad, Sweden, 18-21 June 2018, pp. 345-350.
[http://dx.doi.org/10.1109/CBMS.2018.00067]
[33]
Nawaz, H.; Maqsood, M.; Afzal, S.; Aadil, F.; Mehmood, I.; Rho, S. A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia Tools Appl., 2021, 80(28-29), 35789-35807.
[http://dx.doi.org/10.1007/s11042-020-09087-y]
[34]
Zhang, Y.; Dong, Z.; Phillips, P.; Wang, S.; Ji, G.; Yang, J.; Yuan, T.F. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci., 2015, 9, 66.
[http://dx.doi.org/10.3389/fncom.2015.00066] [PMID: 26082713]
[35]
Fung, G.; Stoeckel, J. SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowl. Inf. Syst., 2007, 11(2), 243-258.
[http://dx.doi.org/10.1007/s10115-006-0043-5]
[36]
Ebrahimi, A.; Luo, S.; Chiong, R.; Initiative, A.D.N. Deep sequence modelling for Alzheimer’s disease detection using MRI. Comput. Biol. Med., 2021, 134, 104537.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104537] [PMID: 34118752]
[37]
Bron, E.E.; Klein, S.; Papma, J.M.; Jiskoot, L.C.; Venkatraghavan, V.; Linders, J.; Aalten, P.; De Deyn, P.P.; Biessels, G.J.; Claassen, J.A.H.R.; Middelkoop, H.A.M.; Smits, M.; Niessen, W.J.; van Swieten, J.C.; van der Flier, W.M.; Ramakers, I.H.G.B.; van der Lugt, A. Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. Neuroimage Clin., 2021, 31, 102712.
[http://dx.doi.org/10.1016/j.nicl.2021.102712] [PMID: 34118592]
[38]
Aderghal, K.; Boissenin, M.; Benois-Pineau, J.; Catheline, G.; Afdel, K. Classification of smri for ad diagnosis with convolutional neuronal networks: A pilot 2-d+ study on adni. Multimedia Modeling: 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4-6, 2017, Proceedings, Part I, Springer, 2016, pp. 690–701.
[39]
Mathotaarachchi, S.; Pascoal, T.A.; Shin, M.; Benedet, A.L.; Kang, M.S.; Beaudry, T.; Fonov, V.S.; Gauthier, S.; Rosa-Neto, P.; Initiative, A.D.N. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol. Aging, 2017, 59, 80-90.
[http://dx.doi.org/10.1016/j.neurobiolaging.2017.06.027] [PMID: 28756942]
[40]
Long, X.; Chen, L.; Jiang, C.; Zhang, L.; Initiative, A.D.N. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One, 2017, 12(3), e0173372.
[http://dx.doi.org/10.1371/journal.pone.0173372] [PMID: 28264071]
[41]
Duraisamy, B.; Shanmugam, J.V.; Annamalai, J. Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network. Brain Imaging Behav., 2019, 13(1), 87-110.
[http://dx.doi.org/10.1007/s11682-018-9831-2] [PMID: 29460167]
[42]
Samper-Gonzalez, J.; Burgos, N.; Fontanella, S.; Bertin, H.; Habert, M-O.; Durrleman, S.; Evgeniou, T.; Colliot, O.; Initiative, A.D.N. Yet another adni machine learning paper? paving the way towards fully- reproducible research on classification of Alzheimer’s disease. Inter- national Workshop on Machine Learning in Medical Imaging, pp. 53–60, vol 10541, Springer, Cham., 21 Sep 2017.
[http://dx.doi.org/10.1007/978-3-319-67389-9_7]
[43]
Westman, E.; Muehlboeck, J.S.; Simmons, A. Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage, 2012, 62(1), 229-238.
[http://dx.doi.org/10.1016/j.neuroimage.2012.04.056] [PMID: 22580170]
[44]
Kruthika, K.; Pai, A.; Maheshappa, H.A. Disease Neuroimaging Initiative, et al., “Classification of Alzheimer and MCI phenotypes on MRI data using SVM. International Symposium on Signal Processing and Intelligent Recognition Systems, Springer, Cham, 27 September 2017, 678, 263–275.
[45]
Fischl, B. FreeSurfer. Neuroimage, 2012, 62(2), 774-781.
[http://dx.doi.org/10.1016/j.neuroimage.2012.01.021] [PMID: 22248573]
[46]
Kim, J.; Lee, M.; Lee, M.K.; Wang, S.M.; Kim, N.Y.; Kang, D.W.; Um, Y.H.; Na, H.R.; Woo, Y.S.; Lee, C.U.; Bahk, W.M.; Kim, D.; Lim, H.K. Development of random forest algorithm based prediction model of alzheimer’s disease using neurodegeneration pattern. Psychiatry Investig., 2021, 18(1), 69-79.
[http://dx.doi.org/10.30773/pi.2020.0304] [PMID: 33561931]
[47]
Lee, J.S.; Kim, C.; Shin, J-H.; Cho, H.; Shin, D.; Kim, N.; Kim, H.J.; Kim, Y.; Lockhart, S.N.; Na, D.L. Machine learning-based in- dividual assessment of cortical atrophy pattern in alzheimer’s disease spectrum: Development of the classifier and longitudinal evaluation. Sci. Rep., 2018, 8(1), 1-10.
[PMID: 29311619]
[48]
Thakare, P.; Pawar, V. Alzheimer disease detection and tracking of alzheimer patient. 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 26-27 August 2016, pp. 1-4
[http://dx.doi.org/10.1109/INVENTIVE.2016.7823286]
[49]
Venkatesh, B.; Anuradha, J. A review of feature selection and its methods. Cybern. Inf. Technol., 2019, 19(1), 3-26.
[http://dx.doi.org/10.2478/cait-2019-0001]
[50]
Sánchez-Maroño, N.; Alonso-Betanzos, A.; Tombilla-Sanromán, M. Filter methods for feature selection–a comparative study. Lect. Notes Comput. Sci., 2007, 4881, 178-187.
[http://dx.doi.org/10.1007/978-3-540-77226-2_19]
[51]
St, L.; Wold, S. Analysis of variance (anova). Chemom. Intell. Lab. Syst., 1989, 6(4), 259-272.
[http://dx.doi.org/10.1016/0169-7439(89)80095-4]
[52]
Saied, I.M.; Arslan, T.; Chandran, S. Classification of alzheimer’s disease using rf signals and machine learning. IEEE J. Electromagn. RF Microw. Med. Biol., 2022, 6(1), 77-85.
[http://dx.doi.org/10.1109/JERM.2021.3096172]
[53]
El-Sappagh, S.; Abuhmed, T.; Alouffi, B.; Sahal, R.; Abdelhade, N.; Saleh, H. The role of medication data to enhance the prediction of Alzheimer’s progression using machine learning. Comput. Intell. Neurosci., 2021, 2021, 8439655.
[54]
Scheubert, L.; Luštrek, M.; Schmidt, R.; Repsilber, D.; Fuellen, G. Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets. BMC Bioinformatics, 2012, 13(1), 266.
[http://dx.doi.org/10.1186/1471-2105-13-266]
[55]
Kavitha, C.; Mani, V.; Srividhya, S.; Khalaf, O.I.; Romero, C.A.T. Early-stage alzheimer’s disease prediction using machine learning models; Frontiers in Public Health, 2022, p. 10.
[56]
Soundarya, S.; Sruthi, M.S.; Sathya Bama, S.; Kiruthika, S.; Dhiyaneswaran, J. Early detection of Alzheimer disease using Gadolinium material. Mater. Today Proc., 2021, 45, 1094-1101.
[http://dx.doi.org/10.1016/j.matpr.2020.03.189]
[57]
Joshi, S.; Shenoy, D.; Simha, G.V.; Rrashmi, P.; Venugopal, K.; Patnaik, L. Classification of Alzheimer’s disease and Parkinson’s disease by using machine learning and neural network methods. 2010 Second International Conference on Machine Learning and Computing, , Bangalore, India, 09-11 February 2010, pp. 218-222.
[http://dx.doi.org/10.1109/ICMLC.2010.45]
[58]
Kaya Keles, M.; Kili¸c, U. Classification of brain volumetric data to determine alzheimer’s disease using artificial bee colony algorithm as feature selector. IEEE Access, 2022, 10, 82989-83001.
[http://dx.doi.org/10.1109/ACCESS.2022.3196649]
[59]
Liu, L.; Zhao, S.; Chen, H.; Wang, A. A new machine learning method for identifying Alzheimer’s disease. Simul. Model. Pract. Theory, 2020, 99, 102023.
[http://dx.doi.org/10.1016/j.simpat.2019.102023]
[60]
Eke, C.S.; Jammeh, E.; Li, X.; Carroll, C.; Pearson, S.; Ifeachor, E. Early detection of alzheimer’s disease with blood plasma proteins using support vector machines. IEEE J. Biomed. Health Inform., 2021, 25(1), 218-226.
[http://dx.doi.org/10.1109/JBHI.2020.2984355] [PMID: 32340968]
[61]
Martinez-Murcia, F.J.; Ortiz, A.; Gorriz, J.M.; Ramirez, J.; Castillo-Barnes, D. Studying the manifold structure of alzheimer’s disease: A deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inform., 2020, 24(1), 17-26.
[http://dx.doi.org/10.1109/JBHI.2019.2914970] [PMID: 31217131]
[62]
Castillo-Barnes, D.; Su, L.; Ramírez, J.; Salas-Gonzalez, D.; Martinez-Murcia, F.J.; Illan, I.A.; Segovia, F.; Ortiz, A.; Cruchaga, C.; Farlow, M.R.; Xiong, C.; Graff-Radford, N.R.; Schofield, P.R.; Masters, C.L.; Salloway, S.; Jucker, M.; Mori, H.; Levin, J.; Gorriz, J.M. Autosomal dominantly inherited Alzheimer disease: Analysis of genetic subgroups by machine learning. Inf. Fusion, 2020, 58, 153-167.
[http://dx.doi.org/10.1016/j.inffus.2020.01.001] [PMID: 32284705]
[63]
Chen, J.; Ma, N.; Hu, G.; Nousayhah, A.; Xue, C.; Qi, W.; Xu, W.; Chen, S.; Rao, J.; Liu, W.; Zhang, F.; Zhang, X. rTMS modulates precuneus-hippocampal subregion circuit in patients with subjective cognitive decline. Aging (Albany NY), 2021, 13(1), 1314-1331.
[http://dx.doi.org/10.18632/aging.202313] [PMID: 33260151]
[64]
Lahmiri, S.; Shmuel, A. Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed. Signal Process. Control, 2019, 52, 414-419.
[http://dx.doi.org/10.1016/j.bspc.2018.08.009]
[65]
Demirhan, A.; Nir, T.M.; Zavaliangos-Petropulu, A.; Jack, C.R.; Weiner, M.W.; Bernstein, M.A.; Thompson, P.M.; Jahanshad, N. Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), , Brooklyn, NY, USA, 16-19 April 2015, pp. 126-130.
[http://dx.doi.org/10.1109/ISBI.2015.7163832]
[66]
Sudharsan, M.; Thailambal, G. Alzheimer’s disease prediction using machine learning techniques and principal component analysis (pca). Mater. Today Proc., 2021.
[67]
Minhas, S.; Khanum, A.; Riaz, F.; Alvi, A.; Khan, S.A.; Initiative, A.D.N. Early Alzheimer’s disease prediction in machine learning setup: Empirical analysis with missing value computation. Springer, Cham. 07 January 2016, 9375, 424–432.
[http://dx.doi.org/10.1007/978-3-319-24834-9_49]
[68]
Naz, S.; Ashraf, A.; Zaib, A. Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimedia Syst., 2022, 28(1), 85-94.
[http://dx.doi.org/10.1007/s00530-021-00797-3]
[69]
Liu, J.; Li, M.; Luo, Y.; Yang, S.; Li, W.; Bi, Y. Alzheimer’s disease detection using depthwise separable convolutional neural networks. Comput. Methods Programs Biomed., 2021, 203, 106032.
[http://dx.doi.org/10.1016/j.cmpb.2021.106032] [PMID: 33713959]
[70]
Sampath, R.; Indumathi, J. Earlier detection of alzheimer disease using N-fold cross validation approach. J. Med. Syst., 2018, 42(11), 217.
[http://dx.doi.org/10.1007/s10916-018-1068-5] [PMID: 30280260]
[71]
Crismon, M.L. Tacrine: First drug approved for alzheimer’s disease. Ann. Pharmacother., 1994, 28(6), 744-751.
[http://dx.doi.org/10.1177/106002809402800612] [PMID: 7919566]
[72]
Cooper, E.L.; Ma, M.J. Alzheimer disease: Clues from traditional and complementary medicine. J. Tradit. Complement. Med., 2017, 7(4), 380-385.
[http://dx.doi.org/10.1016/j.jtcme.2016.12.003] [PMID: 29034183]
[73]
Fang, J.; Wang, L.; Wu, T.; Yang, C.; Gao, L.; Cai, H.; Liu, J.; Fang, S.; Chen, Y.; Tan, W.; Wang, Q. Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment. J. Ethnopharmacol., 2017, 196, 281-292.
[http://dx.doi.org/10.1016/j.jep.2016.11.034] [PMID: 27888133]
[74]
Ali, S.K.; Hamed, A.R.; Soltan, M.M.; Hegazy, U.M.; Elgorashi, E.E.; El-Garf, I.A.; Hussein, A.A. In vitro evaluation of selected Egyptian traditional herbal medicines for treatment of Alzheimer disease. BMC Complement. Altern. Med., 2013, 13(1), 121.
[http://dx.doi.org/10.1186/1472-6882-13-121] [PMID: 23721591]
[75]
Ahmadian-Attari, M.M.; Mosaddegh, M.; Kazemnejad, A.; Noorbala, A.A. Comparison between complementary dietary treatment of alzheimer disease in iranian traditional medicine and modern medicine. Iran. J. Public Health, 2013, 42(12), 1414-1421.
[PMID: 26060643]
[76]
Burlá, C.; Rego, G.; Nunes, R. Alzheimer, dementia and the living will: A proposal. Med. Health Care Philos., 2014, 17(3), 389-395.
[http://dx.doi.org/10.1007/s11019-014-9559-8] [PMID: 24737537]
[77]
Suk, H-I.; Shen, D.; Initiative, A.D.N. Deep learning in diagnosis of brain disorders. In: Recent Progress in Brain and Cognitive Engineering; Springer, 2015; pp. 203-213.
[78]
Cattell, L.; Platsch, G.; Pfeiffer, R.; Declerck, J.; Schnabel, J.A. Initiative, et al., Classification of amyloid status using machine learning with histograms of oriented 3D gradients. In: NeuroImage Clin; , 2016; 12, pp. 990-1003.
[79]
Shankar, K.; Lakshmanaprabu, S.; Khanna, A.; Tanwar, S.; Rodrigues, J.J.; Roy, N.R. Alzheimer detection using group grey wolf optimization based features with convolutional classifier. Comput. Electr. Eng., 2019, 77, 230-243.
[http://dx.doi.org/10.1016/j.compeleceng.2019.06.001]
[80]
Moscoso, A.; Silva-Rodríguez, J.; Aldrey, J.M.; Cortés, J.; Fernández-Ferreiro, A.; Gómez-Lado, N.; Ruibal, Á.; Aguiar, P. Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: Implications for the design of predictive models. Neuroimage Clin., 2019, 23, 101837.
[http://dx.doi.org/10.1016/j.nicl.2019.101837] [PMID: 31078938]
[81]
Rallabandi, V.P.S.; Tulpule, K.; Gattu, M.; Initiative, A.D.N. Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer’s disease using structural MRI analysis. Inform. Med. Unlocked., 2020, 18, 100305.
[http://dx.doi.org/10.1016/j.imu.2020.100305]
[82]
Saratxaga, C.L.; Moya, I.; Picón, A.; Acosta, M.; Moreno-Fernandez-de-Leceta, A.; Garrote, E.; Bereciartua-Perez, A. Mri deep learning- based solution for Alzheimer’s disease prediction. J. Pers. Med., 2021, 11(9), 902.
[http://dx.doi.org/10.3390/jpm11090902] [PMID: 34575679]
[83]
Helaly, H.A.; Badawy, M.; Haikal, A.Y. Deep learning approach for early detection of Alzheimer’s disease. Cognit. Comput., 2022, 14(5), 1711-1727.
[http://dx.doi.org/10.1007/s12559-021-09946-2] [PMID: 34745371]
[84]
Basheer, S.; Bhatia, S.; Sakri, S.B. Computational modeling of dementia prediction using deep neural network: Analysis on oasis dataset. IEEE Access, 2021, 9, 42449-42462.
[http://dx.doi.org/10.1109/ACCESS.2021.3066213]
[85]
Prajapati, R.; Khatri, U.; Kwon, G.R. An efficient deep neural network binary classifier for alzheimer’s disease classification. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Korea (South), 13-16 April 2021, pp. 231-234.
[http://dx.doi.org/10.1109/ICAIIC51459.2021.9415212]
[86]
Kohannim, O.; Hua, X.; Hibar, D.P.; Lee, S.; Chou, Y.Y.; Toga, A.W.; Jack, C.R., Jr; Weiner, M.W.; Thompson, P.M.; Initiative, A.D.N. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol. Aging, 2010, 31(8), 1429-1442.
[http://dx.doi.org/10.1016/j.neurobiolaging.2010.04.022] [PMID: 20541286]
[87]
Ammar, R.B.; Ayed, Y.B. Language-related features for early detection of Alzheimer Disease. Procedia Comput. Sci., 2020, 176, 763-770.
[http://dx.doi.org/10.1016/j.procs.2020.09.071]
[88]
Chitradevi, D.; Prabha, S. Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl. Soft Comput., 2020, 86, 105857.
[http://dx.doi.org/10.1016/j.asoc.2019.105857]
[89]
Suk, H.I.; Lee, S.W.; Shen, D.; Initiative, A.D.N. Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal., 2017, 37, 101-113.
[http://dx.doi.org/10.1016/j.media.2017.01.008] [PMID: 28167394]
[90]
Eitel, F.; Ritter, K. Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification. In: Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support, ; Springer, 2019; pp. 3-11.
[91]
Bairagi, V. Eeg signal analysis for early diagnosis of alzheimer disease using spectral and wavelet-based features. Int. J. Inf. Technol., 2018, 10(3), 403-412.
[92]
Termenon, M.; Besga, A.; Echeveste, J.; Gonzalez-Pinto, A. Alzheimer disease classification on diffusion weighted imaging features. International Work-Conference on the Interplay Between Natural and Artificial Computation, Springer, Berlin, Heidelberg, 2011, 6687, 120–127.
[93]
Chan, H.L.; Li, H.; Lui, L.M. Quasi-conformal statistical shape analysis of hippocampal surfaces for Alzheimer׳s disease analysis. Neurocomputing, 2016, 175, 177-187.
[http://dx.doi.org/10.1016/j.neucom.2015.10.047]
[94]
Mofrad, S.A.; Lundervold, A.; Lundervold, A.S.; Initiative, A.D.N. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer’s disease. Comput. Med. Imaging Graph., 2021, 90, 101910.
[http://dx.doi.org/10.1016/j.compmedimag.2021.101910] [PMID: 33862355]
[95]
Ramzan, F.; Khan, M.U.G.; Rehmat, A.; Iqbal, S.; Saba, T.; Rehman, A.; Mehmood, Z. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting- state fmri and residual neural networks. J. Med. Syst., 2020, 44(2), 37.
[http://dx.doi.org/10.1007/s10916-019-1475-2] [PMID: 31853655]

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