Generic placeholder image

CNS & Neurological Disorders - Drug Targets


ISSN (Print): 1871-5273
ISSN (Online): 1996-3181

Review Article

An Insight into the Role of Artificial Intelligence in the Early Diagnosis of Alzheimer’s Disease

Author(s): Rohit Kumar Verma*, Manisha Pandey, Pooja Chawla*, Hira Choudhury, Jayashree Mayuren, Subrat Kumar Bhattamisra, Bapi Gorain, Maria Abdul Ghafoor Raja, Muhammad Wahab Amjad and Syed Obaidur Rahman

Volume 21, Issue 10, 2022

Published on: 11 May, 2021

Page: [901 - 912] Pages: 12

DOI: 10.2174/1871527320666210512014505

Price: $65


Background: The complication of Alzheimer’s disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the major hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc.) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated.

Methods: This study aimed to overcome the limitation of using artificial intelligence along with technical tools that have been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.

Conclusion: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.

Keywords: Alzheimer’s disease, artificial intelligence, biomarkers, algorithms, AD diagnosis, PET.

Graphical Abstract
Gorain B, Choudhury H, Pandey M, et al. Mechanistic description of natural herbs in the treatment of dementia: A systematic review. Curr Psychopharmacol 2018; 7(2): 149-64.
Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet 2017; 390(10113): 2673-734.
[] [PMID: 28735855]
Liu X, Chen K, Wu T, Weidman D, Lure F, Li J. Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer’s disease. Transl Res 2018; 194: 56-67.
[] [PMID: 29352978]
Yiannopoulou KG, Papageorgiou SG. Current and future treatments in alzheimer disease: an update. J Cent Nerv Syst Dis 2020; 12: 1179573520907397.
[] [PMID: 32165850]
Prince M, Ali GC, Guerchet M, Prina AM, Albanese E, Wu YT. Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimers Res Ther 2016; 8(1): 23.
[] [PMID: 27473681]
Ahmad SS, Khan S, Kamal MA, Wasi U. The Structure and function of α, β and γ-secretase as therapeutic target enzymes in the development of alzheimer’s disease: a review. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2019; 18(9): 657-67.
Iman K, Mirza MU, Mazhar N, Vanmeert M, Irshad I, Kamal MA. In silico structure-based identification of novel acetylcholinesterase inhibitors against alzheimer's disease. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018; 17(1): 54-68.
Ali F, Siddique YH. Bioavailability and pharmaco-therapeutic potential of luteolin in overcoming Alzheimer’s disease. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2019; 18(5): 352-65.
Baek SC, Lee JP, Rangarajan TM, et al. Ethyl acetohydroxamate incorporated chalcones: unveiling a novel class of chalcones for multitarget monoamine oxidase-b inhibitors against alzheimer’s disease. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2019; 18(8): 643-54.
Bais S, Kumari R, Prashar Y. Ameliorative effect of trans-sinapic acid and its protective role in cerebral hypoxia in aluminium chloride induced dementia of Alzheimer's type CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018; 17(2): 144-54.
Beg T, Jyoti S, Naz F, et al. Protective effect of kaempferol on the transgenic Drosophila model of Alzheimer's disease. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018; 17(6): 421-9.
Singh A, Hasan A, Tiwari S, Pandey LM. Therapeutic advancement in alzheimer disease: new hopes on the horizon?. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018; 17(8): 571-89.
Jiang XW, Lu HY, Xu Z, et al. In silico analyses for key genes and molecular genetic mechanism in epilepsy and Alzheimer's disease. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2018; 17(8): 608-17.
Gupta S, Singhal NK, Ganesh S, Sandhir R. Extending arms of insulin resistance from diabetes to Alzheimer’s disease: identification of potential therapeutic targets. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 2019; 18(3): 172-84.
Carrillo MC, Dean RA, Nicolas F, et al. Alzheimer’s association research roundtable. Revisiting the framework of the National Institute on aging-alzheimer’s association diagnostic criteria. Alzheimers Dement 2013; 9(5): 594-601.
[] [PMID: 24007744]
Visser PJ, Vos S, van Rossum I, Scheltens P. Comparison of international working group criteria and national institute on Aging-Alzheimer’s association criteria for Alzheimer’s disease. Alzheimers Dement 2012; 8(6): 560-3.
[] [PMID: 23102126]
Jack CR Jr, Bennett DA, Blennow K, et al. Contributors. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[] [PMID: 29653606]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[] [PMID: 21514250]
Jack CR Jr, Albert MS, Knopman DS, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 257-62.
[] [PMID: 21514247]
Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 280-92.
[] [PMID: 21514248]
O’Sullivan S, Heinsen H, Grinberg LT, et al. The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure. Brain Inform 2019; 6(1): 3.
[] [PMID: 30843118]
Grossi E, Massini G, Buscema M, Savarè R, Maurelli G. Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence. Gend Med 2005; 2(2): 106-17.
[] [PMID: 16115605]
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984; 34(7): 939-44.
[] [PMID: 6610841]
Thakur AK, Kamboj P, Goswami K. Pathophysiology and management of alzheimer’s disease: an overview. J Anal Pharm Res 2018; 9(2): 226-35.
Sarazin M, de Souza LC, Lehéricy S, Dubois B. Clinical and research diagnostic criteria for Alzheimer’s disease. Neuroimaging Clin N Am 2012; 22(1): 23-32, viii.
[] [PMID: 22284731]
Folstein MF. MiniMental State/Folstein MF, Folstein SE, McHugh PR. J Psychiatr Res 1975; 12: 189-98.
[] [PMID: 1202204]
Dos Santos Picanco LC, Ozela PF, de Fatima de Brito Brito M, et al. Alzheimer’s disease: a review from the pathophysiology to diagnosis, new perspectives for pharmacological treatment. Curr Med Chem 2018; 25(26): 3141-59.
[] [PMID: 30191777]
Fagan AM, Mintun MA, Mach RH, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol 2006; 59(3): 512-9.
[] [PMID: 16372280]
Judge D, Roberts J, Khandker RK, Ambegaonkar B, Black CM. Physician practice patterns associated with diagnostic evaluation of patients with suspected mild cognitive impairment and Alzheimer’s disease. Int J Alzheimers Dis 2019; 2019: 4942562.
[] [PMID: 30937189]
Toepper M. Dissociating normal aging from Alzheimer’s disease: A view from cognitive neuroscience. J Alzheimers Dis 2017; 57(2): 331-52.
[] [PMID: 28269778]
Bature F, Guinn BA, Pang D, Pappas Y. Signs and symptoms preceding the diagnosis of Alzheimer’s disease: a systematic scoping review of literature from 1937 to 2016. BMJ Open 2017; 7(8): e015746.
[] [PMID: 28851777]
Dodel R, Belger M, Reed C, et al. Determinants of societal costs in Alzheimer’s disease: GERAS study baseline results. Alzheimers Dement 2015; 11(8): 933-45.
[] [PMID: 25846298]
Zetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer’s disease. Mol Brain 2019; 12(1): 26.
[] [PMID: 30922367]
Acosta-Baena N, Sepulveda-Falla D, Lopera-Gómez CM, et al. Pre-dementia clinical stages in presenilin 1 E280A familial early-onset Alzheimer’s disease: a retrospective cohort study. Lancet Neurol 2011; 10(3): 213-20.
[] [PMID: 21296022]
Bäckman L, Jones S, Berger AK, Laukka EJ, Small BJ. Cognitive impairment in preclinical Alzheimer’s disease: a meta-analysis. Neuropsychology 2005; 19(4): 520-31.
[] [PMID: 16060827]
Abikoff H, Alvir J, Hong G, et al. Logical memory subtest of the Wechsler Memory Scale: age and education norms and alternate- form reliability of two scoring systems. J Clin Exp Neuropsychol 1987; 9(4): 435-48.
[] [PMID: 3597734]
Delis DC, Kaplan E, Kramer JH. Delis-Kaplan executive function system 2001.
Grober E, Lipton RB, Hall C, Crystal H. Memory impairment on free and cued selective reminding predicts dementia. Neurology 2000; 54(4): 827-32.
[] [PMID: 10690971]
Sarazin M, Berr C, De Rotrou J, et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: a longitudinal study. Neurology 2007; 69(19): 1859-67.
[] [PMID: 17984454]
Geerlings MI, Jonker C, Bouter LM, Adèr HJ, Schmand B. Association between memory complaints and incident Alzheimer’s disease in elderly people with normal baseline cognition. Am J Psychiatry 1999; 156(4): 531-7.
[PMID: 10200730]
Jessen F, Wiese B, Bachmann C, et al. German Study on Aging, Cognition and Dementia in Primary Care Patients Study Group. Prediction of dementia by subjective memory impairment: effects of severity and temporal association with cognitive impairment. Arch Gen Psychiatry 2010; 67(4): 414-22.
[] [PMID: 20368517]
Broadbent DE, Cooper PF, FitzGerald P, Parkes KR. The cognitive failures questionnaire (CFQ) and its correlates. Br J Clin Psychol 1982; 21(1): 1-16.
[] [PMID: 7126941]
Hohman TJ, Beason-Held LL, Lamar M, Resnick SM. Subjective cognitive complaints and longitudinal changes in memory and brain function. Neuropsychology 2011; 25(1): 125-30.
[] [PMID: 20919769]
Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62(6): 593-602.
[] [PMID: 15939837]
Ferri CP, Prince M, Brayne C, et al. Alzheimer’s Disease International. Global prevalence of dementia: a Delphi consensus study. Lancet 2005; 366(9503): 2112-7.
[] [PMID: 16360788]
Vinkers DJ, Gussekloo J, Stek ML, Westendorp RG, van der Mast RC. Temporal relation between depression and cognitive impairment in old age: prospective population based study. BMJ 2004; 329(7471): 881.
[] [PMID: 15345599]
Heser K, Tebarth F, Wiese B, et al. Age CoDe Study Group. Age of major depression onset, depressive symptoms, and risk for subsequent dementia: results of the German study on Ageing, Cognition, and Dementia in Primary Care Patients (AgeCoDe). Psychol Med 2013; 43(8): 1597-610.
[] [PMID: 23137390]
Chen P, Ganguli M, Mulsant BH, Dekosky ST. World Health Organization. The temporal relationship between depressive symptoms and dementia. Arch Gen Psychiatry 1999; 56: 261-6.
Brommelhoff JA, Gatz M, Johansson B, McArdle JJ, Fratiglioni L, Pedersen NL. Depression as a risk factor or prodromal feature for dementia? Findings in a population-based sample of Swedish twins. Psychol Aging 2009; 24(2): 373-84.
[] [PMID: 19485655]
Irie F, Masaki KH, Petrovitch H, et al. Apolipoprotein E ε4 allele genotype and the effect of depressive symptoms on the risk of dementia in men: the Honolulu-Asia Aging Study. Arch Gen Psychiatry 2008; 65(8): 906-12.
[] [PMID: 18678795]
Pakhomov SV, Hemmy LS, Lim KO. Automated semantic indices related to cognitive function and rate of cognitive decline. Neuropsychologia 2012; 50(9): 2165-75.
[] [PMID: 22659109]
Weakley A, Schmitter-Edgecombe M, Anderson J. Analysis of verbal fluency ability in amnestic and non-amnestic mild cognitive impairment. Arch Clin Neuropsychol 2013; 28(7): 721-31.
[] [PMID: 23917346]
López-de-Ipiña K, Alonso JB, Travieso CM, et al. On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors (Basel) 2013; 13(5): 6730-45.
[] [PMID: 23698268]
Schubert CR, Carmichael LL, Murphy C, Klein BE, Klein R, Cruickshanks KJ. Olfaction and the 5-year incidence of cognitive impairment in an epidemiological study of older adults. J Am Geriatr Soc 2008; 56(8): 1517-21.
[] [PMID: 18662205]
Schofield PW, Ebrahimi H, Jones AL, Bateman GA, Murray SR. An olfactory ‘stress test’ may detect preclinical Alzheimer’s disease. BMC Neurol 2012; 12(1): 24.
[] [PMID: 22551361]
Pache M, Smeets CH, Gasio PF, et al. Colour vision deficiencies in Alzheimer’s disease. Age Ageing 2003; 32(4): 422-6.
[] [PMID: 12851187]
Crow RW, Levin LB, LaBree L, Rubin R, Feldon SE. Sweep visual evoked potential evaluation of contrast sensitivity in Alzheimer’s dementia. Invest Ophthalmol Vis Sci 2003; 44(2): 875-8.
[] [PMID: 12556424]
Bridenbaugh SA, Kressig RW. Laboratory review: the role of gait analysis in seniors’ mobility and fall prevention. Gerontology 2011; 57(3): 256-64.
[] [PMID: 20980732]
Theill N, Martin M, Schumacher V, Bridenbaugh SA, Kressig RW. Simultaneously measuring gait and cognitive performance in cognitively healthy and cognitively impaired older adults: the Basel motor-cognition dual-task paradigm. J Am Geriatr Soc 2011; 59(6): 1012-8.
[] [PMID: 21649627]
Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol 2006; 5(9): 735-41.
[] [PMID: 16914401]
Mapstone M, Cheema AK, Fiandaca MS, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 2014; 20(4): 415-8.
[] [PMID: 24608097]
Sperling RA, Laviolette PS, O’Keefe K, et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 2009; 63(2): 178-88.
[] [PMID: 19640477]
Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry 2010; 67(6): 584-7.
[] [PMID: 19833321]
Babiloni C, Binetti G, Cassetta E, et al. Mapping distributed sources of cortical rhythms in mild Alzheimer’s disease. A multicentric EEG study. Neuroimage 2004; 22(1): 57-67.
[] [PMID: 15109997]
Tsuno N, Shigeta M, Hyoki K, Faber PL, Lehmann D. Fluctuations of source locations of EEG activity during transition from alertness to sleep in Alzheimer’s disease and vascular dementia. Neuropsychobiology 2004; 50(3): 267-72.
[] [PMID: 15365227]
Buscema M, Rossini P, Babiloni C, Grossi E. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer’s disease patients with high degree of accuracy. Artif Intell Med 2007; 40(2): 127-41.
[] [PMID: 17466496]
Cohen D. Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science 1968; 161(3843): 784-6.
[] [PMID: 5663803]
de Jongh A, de Munck JC, Gonçalves SI, Ossenblok P. Differences in MEG/EEG epileptic spike yields explained by regional differences in signal-to-noise ratios. J Clin Neurophysiol 2005; 22(2): 153-8.
[] [PMID: 15805816]
Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C. Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Neuroimage 2017; 157: 531-44.
[] [PMID: 28619655]
Bastos RKX, Neves JCL, Bevilacqua PD, Silva CV, Carvalho GRM. Avaliação da contaminação de hortaliças irrigadas com esgotos sanitários 2010. Disponível em: http://bvsde. per. paho. org/bvsaidis/mexico26/ii-012. pdfAcessado em, 26
Hillebrand A, Barnes GR, Bosboom JL, Berendse HW, Stam CJ. Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution. Neuroimage 2012; 59(4): 3909-21.
[] [PMID: 22122866]
Poza J, Hornero R, Escudero J, Fernández A, Sánchez CI. Regional analysis of spontaneous MEG rhythms in patients with Alzheimer’s disease using spectral entropies. Ann Biomed Eng 2008; 36(1): 141-52.
[] [PMID: 17994279]
Alonso JF, Poza J, Mañanas MA, Romero S, Fernández A, Hornero R. MEG connectivity analysis in patients with Alzheimer’s disease using cross mutual information and spectral coherence. Ann Biomed Eng 2011; 39(1): 524-36.
[] [PMID: 20824340]
Buldú JM, Bajo R, Maestú F, et al. Reorganization of functional networks in mild cognitive impairment. PLoS One 2011; 6(5): e19584.
[] [PMID: 21625430]
Inglis B, Buckenmaier K, Sangiorgio P, Pedersen AF, Nichols MA, Clarke J. MRI of the human brain at 130 microtesla. Proc Natl Acad Sci USA 2013; 110(48): 19194-201.
[] [PMID: 24255111]
Sander TH, Preusser J, Mhaskar R, Kitching J, Trahms L, Knappe S. Magnetoencephalography with a chip-scale atomic magnetometer. Biomed Opt Express 2012; 3(5): 981-90.
[] [PMID: 22567591]
Yang S, Bornot JMS, Wong-Lin K, Prasad G. M/EEG-based bio- markers to predict the MCI and alzheimer’s disease: a review from the ML perspective. IEEE Trans Biomed Eng 2019; 66(10): 2924-35.
[] [PMID: 30762522]
Graham SA, Lee EE, Jeste DV, et al. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Res 2020; 284: 112732.
[] [PMID: 31978628]
Biddle KD, d’Oleire Uquillas F, Jacobs HIL, et al. Social engagement and amyloid-β-related cognitive decline in cognitively normal older adults. Am J Geriatr Psychiatry 2019; 27(11): 1247-56.
[] [PMID: 31248770]
Linggonegoro DW, Torous J. Expanding technology for engagement in dementia while ensuring equity, interoperability, and privacy. Int Psychogeriatr 2020; 32(8): 893-5.
[] [PMID: 32933603]
Cheng ST. Dementia caregiver burden: a research update and critical analysis. Curr Psychiatry Rep 2017; 19(9): 64.
[] [PMID: 28795386]
Feast A, Moniz-Cook E, Stoner C, Charlesworth G, Orrell M. A systematic review of the relationship between behavioral and psychological symptoms (BPSD) and caregiver well-being. Int Psychogeriatr 2016; 28(11): 1761-74.
[] [PMID: 27345942]
Meeks TW, Jeste DV. Neurobiology of wisdom: a literature overview. Arch Gen Psychiatry 2009; 66(4): 355-65.
[] [PMID: 19349305]
Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital biomarkers for the early detection of mild cognitive impairment: artificial intelligence meets virtual reality. Front Hum Neurosci 2020; 14: 245.
[] [PMID: 32848660]
Seo K, Kim JK, Oh DH, Ryu H, Choi H. Virtual daily living test to screen for mild cognitive impairment using kinematic movement analysis. PLoS One 2017; 12(7): e0181883.
[] [PMID: 28738088]
Duda RO, Hart PE, Stork DG. Pattern Classification. New York: John Wiley & Sons. Inc. 2001; p. 2.
Mitchell TM. Machine learning.Burr Ridge. IL: McGraw Hill 1997; 45: pp. (37)870-7.
Chaves R, Ramírez J, Górriz JM, et al. Effective diagnosis of Alzheimer’s disease by means of association rules. International Conference on Hybrid Artificial Intelligence Systems. 452-9.
Weng G, Bhalla US, Iyengar R. Complexity in biological signaling systems. Science 1999; 284(5411): 92-6.
[] [PMID: 10102825]
Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2001; 2(1): 343-72.
[] [PMID: 11701654]
Castrillo JI, Lista S, Hampel H, Ritchie CW. Systems biology methods for Alzheimer’s disease research toward molecular signatures, subtypes, and stages and precision medicine: application in cohort studies and trials.Biomarkers for Alzheimer’s Disease Drug Development. New York, NY: Humana Press 2018; pp. 31-66.
Baker RE, Peña JM, Jayamohan J, Jérusalem A. Mechanistic models versus machine learning, a fight worth fighting for the biological community? Biol Lett 2018; 14(5): 20170660.
[] [PMID: 29769297]
Sachs K, Perez O, Pe’er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single- cell data. Science 2005; 308(5721): 523-9.
[] [PMID: 15845847]
Kell DB. Theodor Bücher Lecture. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest. FEBS J 2006; 273(5): 873-94.
[] [PMID: 16478464]
Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell 2018; 173(7): 1581-92.
[] [PMID: 29887378]
Subasi A. Use of artificial intelligence in Alzheimer’s disease detection. Artificial Intelligence in Precision Health. Academic Press 2020; pp. 257-78.
Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2007; 2: 59-77.
[PMID: 19458758]
Klöppel S, Kotschi M, Peter J, et al. Alzheimer’s Disease Neuroimaging Initiative. Separating symptomatic Alzheimer’s disease from depression based on structural MRI. J Alzheimers Dis 2018; 63(1): 353-63.
[] [PMID: 29614658]
Chaves R, Ramírez J, Górriz JM, Puntonet CG. Association rule-based feature selection method for Alzheimer’s disease diagnosis. Expert Syst Appl 2012; 39(14): 11766-74.
Veeramuthu A, Meenakshi S, Manjusha PS. A New Approach for Alzheimer’s Disease Diagnosis by using Association Rule over PET Images. Int J Comput Appl 2014; 91(9).
Chaves R, Ramírez J, Gorriz JM. Integrating discretization and association rule-based classification for Alzheimer’s disease diagnosis. Expert Syst Appl 2013; 40(5): 1571-8.
Liu M, Zhang D, Shen D. Alzheimer’s disease neuroimaging initiative. Ensemble sparse classification of Alzheimer’s disease. Neuroimage 2012; 60(2): 1106-16.
[] [PMID: 22270352]
Klöppel S, Stonnington CM, Chu C, et al. Automatic classification of MR scans in Alzheimer’s disease. Brain 2008; 131(Pt 3): 681-9.
[] [PMID: 18202106]
Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Alzheimer’s disease neuroimaging initiative. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011; 55(3): 856-67.
[] [PMID: 21236349]
Westman E, Muehlboeck JS, Simmons A. Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage 2012; 62(1): 229-38.
[] [PMID: 22580170]
Cuingnet R, Gerardin E, Tessieras J, et al. Alzheimer's Disease Neuroimaging Initiative, 2011. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011; 56(2): 766-81.
Kohannim O, Hua X, Hibar DP, et al. Alzheimer’s Disease Neuroimaging Initiative. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 2010; 31(8): 1429-42.
[] [PMID: 20541286]
Defrise M, Rezaei A, Nuyts J. Transmission-less attenuation correction in time-of-flight PET: analysis of a discrete iterative algorithm. Phys Med Biol 2014; 59(4): 1073-95.
[] [PMID: 24504259]
Polikar R, Tilley C, Hillis B, Clark CM. Multimodal EEG, MRI and PET data fusion for Alzheimer's disease diagnosis. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. 6058-61.
Parikh D, Polikar R. An ensemble-based incremental learning approach to data fusion. IEEE Trans Syst Man Cybern B Cybern 2007; 37(2): 437-50.
[] [PMID: 17416170]
Khan A, Usman M. Early diagnosis of Alzheimer’s disease using machine learning techniques: A review paper. 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K). Vol. 1: 380-7.
Ibrahim AM, Pottoo FH, Dahiya ES, Khan FA, Kumar JBS. Neuron-glia interactions: Molecular basis of alzheimer’s disease and applications of neuroproteomics. Eur J Neurosci 2020; 52(2): 2931-43.
[] [PMID: 32463535]

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy