Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.
Alzheimer's disease
artificial intelligence (AI)
dementia
machine learning (ML)
neurodegenerative diseases
neuroimaging
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
10 Aug 2023
10 Aug 2023
Historique:
revised:
18
05
2023
received:
22
11
2022
accepted:
02
06
2023
pubmed:
11
8
2023
medline:
11
8
2023
entrez:
11
8
2023
Statut:
aheadofprint
Résumé
Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Medical Research Council
ID : MR/X005674/1
Pays : United Kingdom
Informations de copyright
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Références
Fischer CE, Qian W, Schweizer TA, et al. Determining the impact of psychosis on rates of false-positive and false-negative diagnosis in Alzheimer's disease. Alzheimers Dement Transl Res Clin Interv. 2017;3:385-392. doi:10.1016/j.trci.2017.06.001
Cook LD, Nichol KE, Isaacs JD. The London memory service audit and quality improvement programme. BJPsych Bull. 2019;43:215-220. doi:10.1192/bjb.2019.18
Nedelec T, Couvy-Duchesne B, Monnet F, et al. Identifying health conditions associated with Alzheimer's disease up to 15 years before diagnosis: an agnostic study of French and British health records. Lancet Digit Health. 2022;4:e169-78. doi:10.1016/S2589-7500(21)00275-2
de Vugt ME, Verhey FRJ. The impact of early dementia diagnosis and intervention on informal caregivers. Prog Neurobiol. 2013;110:54-62. doi:10.1016/j.pneurobio.2013.04.005
Robinson L, Tang E, Taylor J-P. Dementia: timely diagnosis and early intervention. BMJ. 2015;350:h3029. doi:10.1136/bmj.h3029
Meco AD, Vassar R. Early detection and personalized medicine: future strategies against Alzheimer's disease. Prog Mol Biol Transl Sci. 2021;177:157-173. doi:10.1016/bs.pmbts.2020.10.002
Rittman T. Neurological update: neuroimaging in dementia. J Neurol. 2020;267:3429-3435. doi:10.1007/s00415-020-10040-0
Filippi M, Agosta F, Barkhof F, et al. EFNS task force: the use of neuroimaging in the diagnosis of dementia. Eur J Neurol. 2012;19:1487-1501. doi:10.1111/j.1468-1331.2012.03859.x
Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry. 2014;85:692-698. doi:10.1136/jnnp-2013-306285
Karas GB, Burton EJ, Rombouts SA, et al. A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry. NeuroImage. 2003;18:895-907. doi:10.1016/s1053-8119(03)00041-7
Young AL, Marinescu RV, Oxtoby NP, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun. 2018;9:4273. doi:10.1038/s41467-018-05892-0
Pievani M, de Haan W, Wu T, Seeley WW, Frisoni GB. Functional network disruption in the degenerative dementias. Lancet Neurol. 2011;10:829-843. doi:10.1016/S1474-4422(11)70158-2
Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci. 2004;101:4637-4642. doi:10.1073/pnas.0308627101
Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P. Resting-state network dysfunction in Alzheimer's disease: a systematic review and meta-analysis. Alzheimers Dement Amst Neth. 2017;8:73-85. doi:10.1016/j.dadm.2017.03.007
Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306-319. doi:10.1002/ana.20009
Lowe VJ, Curran G, Fang P, et al. An autoradiographic evaluation of AV-1451 Tau PET in dementia. Acta Neuropathol Commun. 2016;4. doi:10.1186/s40478-016-0315-6
Chételat G, Arbizu J, Barthel H, et al. Amyloid-PET and 18 F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol. 2020;19:951-962. doi:10.1016/S1474-4422(20)30314-8
Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol. 1995;242:557-560. doi:10.1007/BF00868807
Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32:1318-1322. doi:10.1161/01.str.32.6.1318
Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149:351-356. doi:10.2214/ajr.149.2.351
Wang J, Zuo X, He Y. Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci. 2010;4. https://pubmed.ncbi.nlm.nih.gov/20589099/
Davatzikos C. Machine learning in neuroimaging: progress and challenges. NeuroImage. 2019;197:652-656. doi:10.1016/j.neuroimage.2018.10.003
Li X, Xiong H, Li X, et al. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowl Inf Syst. 2022;64:3197-3234. doi:10.1007/s10115-022-01756-8
Hainc N, Federau C, Stieltjes B, Blatow M, Bink A, Stippich C. The bright, artificial intelligence-augmented future of neuroimaging reading. Front Neurol. 2017;8:489. doi:10.3389/fneur.2017.00489
Kohoutová L, Heo J, Cha S, et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat Protoc. 2020;15:1399-1435. doi:10.1038/s41596-019-0289-5
Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5:791-798. doi:10.1016/j.bpsc.2019.11.007
Mueller SG, Weiner MW, Thal LJ, et al. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement J Alzheimers Assoc. 2005;1:55-66. doi:10.1016/j.jalz.2005.06.003
Marzi SJ, Nott A, Sala Frigerio C, et al. Artificial intelligence for neurodegenerative experimental models. Alzheimer's Dementia. Submitted.
Doherty T, Yao Z, Al Khleifat A, et al. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimer's Dementia.
Bettencourt C, Skene N, Bandres-Ciga S, et al. Artificial intelligence for dementia genetics and omics. Alzheimer's Dementia. Submitted.
Winchester LM, Harshfield EL, Shi L, et al. Artificial intelligence for alzheimer's disease and associated dementia biomarkers. Alzheimer's Dementia. Submitted.
Newby D, Orgeta V, Marshall CR, et al. Artificial intelligence for dementia prevention. Alzheimer's Dementia. Submitted.
Lyall DM, Kormilitzin A, Lancaster C, et al. Artificial intelligence for dementia applied models and digital health. Alzheimer's Dementia. Submitted.
Bucholc M, James C, Al Khleifat A, et al. Artificial intelligence for dementia research methods optimization. Alzheimer's Dementia. Submitted.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
Moola S, Munn Z, Tufanaru C, et al. Chapter 7: systematic reviews of etiology and risk. 2019. doi:10.46658/JBIRM-17-06
Bishop CM. Probabilistic Generative Models (section 4.2). Pattern Recognit. Mach. Learn. Newer. Springer-Verlag New York Inc.; 2007.
Bishop CM. Probabilistic Discriminative Models (section 4.3). Pattern Recognit. Mach. Learn. Newer. Springer-Verlag New York Inc.; 2007.
Bishop CM. Sparse Kernel Machines Pattern Recognition and Machine Learning (Chapter 7). Pattern Recognit. Mach. Learn. Newer. Springer-Verlag New York Inc.; 2007.
Franc V, Zien A, Schölkopf B, Support Vector Machines as Probabilistic Models. 2011.
Murphy K. A probabilistic interpretation of SVMs. Mach. Learn. Probabilistic Perspect. MIT Press; 2012.
Pellegrini E, Ballerini L, Hernandez M del CV, et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimers Dement Diagn Assess Dis Monit. 2018;10:519-535. doi:10.1016/j.dadm.2018.07.004
LeBlanc M, Zuber V, Thompson WK, et al. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics. 2018;19:494. doi:10.1186/s12864-018-4859-7
Han B, Duong D, Sul JH, de Bakker PIW, Eskin E, Raychaudhuri S. A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Hum Mol Genet. 2016;25:1857-1866. doi:10.1093/hmg/ddw049
Jain R, Jain N, Aggarwal A, Hemanth DJ. Convolutional neural network based Alzheimer's disease classification from magnetic resonance brain images. Cogn Syst Res. 2019;57:147-159. doi:10.1016/j.cogsys.2018.12.015
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Alzheimer's Disease Neuroimaging I. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. Med Image Anal. 2018;46:26-34.
Li W, Zhang L, Qiao L, Shen D. Toward a better estimation of functional brain network for mild cognitive impairment identification: a transfer learning view. IEEE J Biomed Health Inform. 2020;24:1160-1168. doi:10.1109/JBHI.2019.2934230
Nanni L, Interlenghi M, Brahnam S, et al. Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer's Disease. Front Neurol. 2020;11.
Li T-R, Wu Y, Jiang J-J, et al. Radiomics analysis of magnetic resonance imaging facilitates the identification of preclinical Alzheimer's Disease: an exploratory study. Front Cell Dev Biol. 2020;0. doi:10.3389/fcell.2020.605734
de Vos F, Schouten TM, Koini M, et al. Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients. NeuroImage Clin. 2020;27:102303. doi:10.1016/j.nicl.2020.102303
Rabin JS, Neal TE, Nierle HE, et al. Multiple markers contribute to risk of progression from normal to mild cognitive impairment. NeuroImage Clin. 2020;28:102400. doi:10.1016/j.nicl.2020.102400
Li F, Liu M. Alzheimer's disease neuroimaging initiative. A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer's disease. J Neurosci Methods. 2019;323:108-118. doi:10.1016/j.jneumeth.2019.05.006
Morin A, Samper-Gonzalez J, Bertrand A, et al. Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort. J Alzheimers Dis JAD. 2020;74:1157-1166. doi:10.3233/JAD-190594
Costafreda SG, Dinov ID, Tu Z, et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. NeuroImage. 2011;56:212-219. doi:10.1016/j.neuroimage.2011.01.050
Guo Y, Zhang Z, Zhou B, et al. Grey-matter volume as a potential feature for the classification of Alzheimer's disease and mild cognitive impairment: an exploratory study. Neurosci Bull. 2014;30:477-489. doi:10.1007/s12264-013-1432-x
Cárdenas-Peña D, Collazos-Huertas D, Castellanos-Dominguez G. Enhanced data representation by kernel metric learning for dementia diagnosis. Front Neurosci. 2017;11:413. doi:10.3389/fnins.2017.00413
Klöppel S, Peter J, Ludl A, et al. Applying automated MR-based diagnostic methods to the memory clinic: a prospective study. J Alzheimers Dis JAD. 2015;47:939-954. doi:10.3233/JAD-150334
Cheng B, Liu M, Shen D, Li Z, Zhang D. Alzheimer's disease neuroimaging initiative. multi-domain transfer learning for early diagnosis of Alzheimer's disease. Neuroinformatics. 2017;15:115-132. doi:10.1007/s12021-016-9318-5
Coupé P, Fonov VS, Bernard C, et al. Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: toward an early individual prognosis. Hum Brain Mapp. 2015;36:4758-4770. doi:10.1002/hbm.22926
Gorji HT, Haddadnia J. A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI. Neuroscience. 2015;305:361-371. doi:10.1016/j.neuroscience.2015.08.013
Dai Z, Yan C, Wang Z, et al. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). NeuroImage. 2012;59:2187-2195. doi:10.1016/j.neuroimage.2011.10.003
Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A. Identification of the early stage of Alzheimer's disease using structural MRI and resting-state fMRI. Front Neurol. 2019;10:904. doi:10.3389/fneur.2019.00904
Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F. Early diagnosis of Alzheimer`s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing. 2015;151:139-150. doi:10.1016/j.neucom.2014.09.072
Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early detection of Alzheimer's disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci. 2020;14.
Salvatore C, Cerasa A, Battista P, et al. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. Front Neurosci. 2015;9:307. doi:10.3389/fnins.2015.00307
Chincarini A, Bosco P, Calvini P, et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. NeuroImage. 2011;58:469-480. doi:10.1016/j.neuroimage.2011.05.083
Hu K, Wang Y, Chen K, Hou L, Zhang X. Multi-scale features extraction from baseline structure MRI for MCI patient classification and AD early diagnosis. Neurocomputing. 2016;175:132-145. doi:10.1016/j.neucom.2015.10.043
Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Alzheimer's Disease Neuroimaging Initiative. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. NeuroImage. 2015;104:398-412. doi:10.1016/j.neuroimage.2014.10.002
Zhu Y, Kim M, Zhu X, Kaufer D, Wu G. Alzheimer's disease neuroimaging initiative. Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data. Med Image Anal. 2021;67:101825. doi:10.1016/j.media.2020.101825
Li H, Fan Y. Early prediction of alzheimer's disease dementia based on baseline hippocampal mri and 1-year follow-up cognitive measures using deep recurrent neural networks. Proc IEEE Int Symp Biomed Imaging. 2019;2019:368-371. doi:10.1109/ISBI.2019.8759397
Lisowska A, Rekik I. Joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis. Brain Connect. 2019;9:22-36. doi:10.1089/brain.2018.0578
Singanamalli A, Wang H, Madabhushi A. Cascaded multi-view canonical correlation (CaMCCo) for early diagnosis of Alzheimer's disease via fusion of clinical, imaging and omic features. Sci Rep. 2017;7:8137. doi:10.1038/s41598-017-03925-0
Davatzikos C, Xu F, An Y, Fan Y, Resnick SM. Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain J Neurol. 2009;132:2026-2035. doi:10.1093/brain/awp091
Chincarini A, Sensi F, Rei L, et al. Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease. NeuroImage. 2016;125:834-847. doi:10.1016/j.neuroimage.2015.10.065
Cui R, Liu M. Alzheimer's Disease Neuroimaging Initiative. RNN-based longitudinal analysis for diagnosis of Alzheimer's disease. Comput Med Imaging Graph Off J Comput Med Imaging Soc. 2019;73:1-10. doi:10.1016/j.compmedimag.2019.01.005
Farzan A, Mashohor S, Ramli R, Mahmud R. Discriminant analysis of intermediate brain atrophy rates in longitudinal diagnosis of alzheimer's disease. Diagn Pathol. 2011;6:105. doi:10.1186/1746-1596-6-105
Zhang D, Shen D, Initiative ADN. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLOS ONE. 2012;7:e33182. doi:10.1371/journal.pone.0033182
Castellazzi G, Cuzzoni MG, Cotta Ramusino M, et al. A machine learning approach for the differential diagnosis of alzheimer and vascular dementia fed by MRI selected features. Front Neuroinformatics. 2020;0. doi:10.3389/fninf.2020.00025
Li Q, Wu X, Xu L, Chen K, Yao L. Classification of Alzheimer's disease, mild cognitive impairment, and cognitively unimpaired individuals using multi-feature kernel discriminant dictionary learning. Front Comput Neurosci. 2018;11:no pagination.
Jung WB, Lee YM, Kim YH, Mun CW. Automated classification to predict the progression of alzheimer's disease using whole-brain volumetry and DTI. Psychiatry Investig. 2015;12:92-102.
Ebadi A, Dalboni da Rocha JL, Nagaraju DB, et al. Ensemble classification of Alzheimer's disease and mild cognitive impairment based on complex graph measures from diffusion tensor images. Front Neurosci. 2017;11.
Kruthika KR, Rajeswari, Maheshappa HD. CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis. Inform Med Unlocked. 2019;14:59-68.
Gao F, Yoon H, Xu Y, et al. AD-NET: age-adjust neural network for improved MCI to AD conversion prediction. NeuroImage Clin. 2020;27:no pagination.
Hett K, Ta V-T, Manjón JV, Coupé P. Adaptive fusion of texture-based grading for Alzheimer's disease classification. Comput Med Imaging Graph Off J Comput Med Imaging Soc. 2018;70:8-16. doi:10.1016/j.compmedimag.2018.08.002
Eskildsen SF, Coupe P, Garcia-Lorenzo D, et al. Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage. 2013;65:511-521.
Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A. Alzheimer's Disease Neuroimaging I. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J Neurosci Methods. 2017;282:69-80.
Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. Comput Biol Med. 2018;102:30-39. doi:10.1016/j.compbiomed.2018.09.004
Li Y, Yang H, Lei B, Liu J, Wee CY. Novel effective connectivity inference using ultra-group constrained orthogonal forward regression and elastic multilayer perceptron classifier for MCI identification. IEEE Trans Med Imaging. 2019;38:1227-1239.
Nguyen DT, Ryu S, Qureshi MNI, Choi M, Lee KH, Lee B. Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns. PLoS ONE Electron Resour. 2019;14:e0212582.
Jin D, Wang P, Zalesky A, et al. Grab-AD: generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease. Hum Brain Mapp. 2020;41:3379-3391.
Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D. Spatial-temporal dependency modeling and network hub detection for functional MRI analysis via convolutional-recurrent network. IEEE Trans Biomed Eng. 2020;67:2241-2252.
Garn H, Coronel C, Waser M, Caravias G, Ransmayr G. Differential diagnosis between patients with probable Alzheimer's disease, Parkinson's disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features. J Neural Transm Vienna Austria. 1996 2017;124:569-581. doi:10.1007/s00702-017-1699-6
Ruffini G, Ibañez D, Castellano M, et al. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol. 2019;10.
Dottori M, Sedeño L, Martorell Caro M, et al. Towards affordable biomarkers of frontotemporal dementia: a classification study via network's information sharing. Sci Rep. 2017;7:3822. doi:10.1038/s41598-017-04204-8
Furutani N, Nariya Y, Takahashi T, et al. Decomposed temporal complexity analysis of neural oscillations and machine learning applied to Alzheimer's disease diagnosis. Front Psychiatry. 2020;11.
Chapman RM, McCrary JW, Gardner MN, et al. Brain ERP components predict which individuals progress to Alzheimer's disease and which do not. Neurobiol Aging. 2011;32:1742-1755. doi:10.1016/j.neurobiolaging.2009.11.010
Holler Y, Bathke AC, Uhl A, et al. Combining SPECT and quantitative EEG analysis for the automated differential diagnosis of disorders with amnestic symptoms. Front Aging Neurosci. 2017;9.
Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG. NeuroImage. 2010;49:668-693. doi:10.1016/j.neuroimage.2009.06.056
Cichocki A, Shishkin SL, Musha T, Leonowicz Z, Asada T, Kurachi T. EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease. Clin Neurophysiol Off J Int Fed Clin Neurophysiol. 2005;116:729-737. doi:10.1016/j.clinph.2004.09.017
Buscema M, Vernieri F, Massini G, et al. An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features. Artif Intell Med. 2015;64:59-74. doi:10.1016/j.artmed.2015.03.003
Gallego-Jutglà E, Solé-Casals J, Vialatte F-B, Elgendi M, Cichocki A, Dauwels J. A hybrid feature selection approach for the early diagnosis of Alzheimer's disease. J Neural Eng. 2015;12:016018. doi:10.1088/1741-2560/12/1/016018
Toussaint P-J, Perlbarg V, Bellec P, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. NeuroImage. 2012;63:936-946. doi:10.1016/j.neuroimage.2012.03.091
Gray K, Wolz R, Heckemann R, Rueckert D, Hammers A. Structural differences in cognitively normal elderly individuals with abnormal amyloid biomarkers: detection using volumetric MRI in ADNI and AIBL. Alzheimers Dement. 2012(1):P337-8.
De Carli F, Nobili F, Pagani M, et al. Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease. Eur J Nucl Med Mol Imaging. 2019;46:334-347. doi:10.1007/s00259-018-4197-7
Ferreira LK, Rondina JM, Kubo R, et al. Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals. Rev Bras Psiquiatr. 2018;40:181-191.
Fan Y, Resnick SM, Wu X, Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. NeuroImage. 2008;41:277-285. doi:10.1016/j.neuroimage.2008.02.043
Pardo JV, Lee JT, Kuskowski MA, et al. Fluorodeoxyglucose positron emission tomography of mild cognitive impairment with clinical follow-up at 3 years. Alzheimers Dement. 2010;6:326-333.
Pan X, Adel M, Fossati C, Gaidon T, Wojak J, Guedj E. Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease. Comput Methods Programs Biomed. 2019;180:no pagination.
Ortiz A, Munilla J, Alvarez-Illan I, Gorriz JM, Ramirez J. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's disease diagnosis. Front Comput Neurosci. 2015;9:1-18.
Li Y, Lu J, Jiang J, Zhang H, Zuo C. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Ther Adv Neurol Disord. 2019;12.
Teng L, Li Y, Zhao Y, et al. Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study. BMC Neurol. 2020;20:148.
Cabral C, Morgado PM, Campos Costa D, Silveira M. Alzheimer's Disease Neuroimaging I. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med. 2015;58:101-109.
Shen T, Jiang J, Lu J, et al. Predicting Alzheimer disease from mild cognitive impairment with a deep belief network based on 18F-FDG-PET images. Mol Imaging. 2019;18:1536012119877285.
Ota K, Oishi N, Ito K, Fukuyama H, SEAD-J Study Group. Alzheimer's Disease Neuroimaging Initiative. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease. J Neurosci Methods. 2015;256:168-183. doi:10.1016/j.jneumeth.2015.08.020
Zhan Y, Chen K, Wu X, et al. Identification of conversion from normal elderly cognition to Alzheimer's disease using multimodal support vector machine. J Alzheimers Dis. 2015;47:1057-1067.
Ben Bouallègue F, Mariano-Goulart D, Payoux P, Alzheimer's Disease Neuroimaging Initiative (ADNI). Joint Assessment of Quantitative 18F-Florbetapir and 18F-FDG Regional Uptake Using Baseline Data from the ADNI. J Alzheimers Dis JAD. 2018;62:399-408. doi:10.3233/JAD-170833
Yang BH, Chen JC, Chou WH, et al. Classification of Alzheimer's Disease from 18F-FDG and 11C-PiB PET imaging biomarkers using support vector machine. J Med Biol Eng. 2020;40:545-554.
Liu F, Wee C-Y, Chen H, Shen D. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification. NeuroImage. 2014;84:466-475. doi:10.1016/j.neuroimage.2013.09.015
El-Gamal FEA, Elmogy MM, Ghazal M, et al. A novel early diagnosis system for mild cognitive impairment based on local region analysis: a pilot study. Front Hum Neurosci. 2018;11:no pagination.
Xu L, Wu X, Chen K, Yao L. Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment. Comput Methods Programs Biomed. 2015;122:182-190.
Nozadi SH, Kadoury S. Classification of Alzheimer's and MCI patients from semantically parcelled PET images: a comparison between AV45 and FDG-PET. Int J Biomed Imaging. 2018;2018:no pagination.
Choi H, Jin KH. Alzheimer's Disease Neuroimaging I. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103-109.
Ding Y, Sohn JH, Kawczynski MG, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the Brain. Radiology. 2019;290:456-464.
Huang Y, Xu J, Zhou Y, Tong T, Zhuang X. Diagnosis of Alzheimer's disease via multi-modality 3D convolutional neural network. Front Neurosci. 2019;13.
Son HJ, Oh JS, Oh M, et al. The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases. Eur J Nucl Med Mol Imaging. 2020;47:332-341.
Blazhenets G, Ma Y, Sorensen A, et al. Principal components analysis of brain metabolism predicts development of Alzheimer dementia. J Nucl Med. 2019;60:837-843.
Morgado P, Silveira M, Marques JS. Diagnosis of Alzheimer's disease using 3D local binary patterns. Comput Methods Biomech Biomed Eng Imaging Vis. 2013;1:2-12.
Popuri K, Balachandar R, Alpert K, et al. Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging. NeuroImage Clin. 2018;18:802-813.
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Alzheimer's disease neuroimaging I. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer's disease using structural MR and FDG-PET images. Sci Rep. 2018;8:5697.
Liu M, Cheng D, Yan W. Alzheimer's disease neuroimaging initiative. classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinformatics. 2018;12.
Suk H-I, Lee S-W, Shen D. Alzheimer's Disease Neuroimaging Initiative. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct. 2015;220:841-859. doi:10.1007/s00429-013-0687-3
Zhang F, Li Z, Zhang B, Du H, Wang B, Zhang X. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease. Neurocomputing. 2019;361:185-195. doi:10.1016/j.neucom.2019.04.093
Shao W, Peng Y, Zu C, Wang M, Zhang D. Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease. Comput Med Imaging Graph. 2020;80:no pagination.
Zu C, Jie B, Liu M, et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav. 2016;10:1148-1159.
Liu L, Fu L, Zhang X, et al. Combination of dynamic (11)C-PIB PET and structural MRI improves diagnosis of Alzheimer's disease. Psychiatry Res. 2015;233:131-140. doi:10.1016/j.pscychresns.2015.05.014
Giorgio J, Landau SM, Jagust WJ, Tino P, Kourtzi Z. Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease. NeuroImage Clin. 2020;26:102199. doi:10.1016/j.nicl.2020.102199
Borroni B, Anchisi D, Paghera B, et al. Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging. 2006;27:24-31. doi:10.1016/j.neurobiolaging.2004.12.010
Habert M-O, Horn J-F, Sarazin M, et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer's disease among patients with mild cognitive impairment. Neurobiol Aging. 2011;32:15-23. doi:10.1016/j.neurobiolaging.2009.01.013
Segovia F, Bastin C, Salmon E, Górriz JM, Ramírez J, Phillips C. Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer's disease. PloS One. 2014;9:e88687. doi:10.1371/journal.pone.0088687
Bhagwat N, Pipitone J, Voineskos AN, Chakravarty MM. Alzheimer's Disease Neuroimaging Initiative. An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures. J Psychiatry Neurosci JPN. 2019;44:246-260. doi:10.1503/jpn.180016
Zhou J, Liu J, Narayan VA, Ye J. Modeling disease progression via multi-task learning. NeuroImage. 2013;78:233-248. doi:10.1016/j.neuroimage.2013.03.073
Lei B, Hou W, Zou W, Li X, Zhang C, Wang T. Longitudinal score prediction for Alzheimer's disease based on ensemble correntropy and spatial-temporal constraint. Brain Imaging Behav. 2019;13:126-137. doi:10.1007/s11682-018-9834-z
Battineni G, Chintalapudi N, Amenta F, Traini E. A comprehensive machine-learning model applied to magnetic resonance imaging (MRI) to predict Alzheimer's disease (AD) in older subjects. J Clin Med. 2020;9:2146. doi:10.3390/jcm9072146
Bachli MB, Sedeño L, Ochab JK, et al. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: a machine learning approach. NeuroImage. 2020;208:116456. doi:10.1016/j.neuroimage.2019.116456
Cajanus A, Hall A, Koikkalainen J, et al. Automatic MRI quantifying methods in behavioral-variant frontotemporal dementia diagnosis. Dement Geriatr Cogn Disord Extra. 2018;8:51-59. doi:10.1159/000486849
Möller C, Pijnenburg YAL, van der Flier WM, et al. Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis. Radiology. 2016;279:838-848. doi:10.1148/radiol.2015150220
Perry DC, Brown JA, Possin KL, et al. Clinicopathological correlations in behavioural variant frontotemporal dementia. Brain J Neurol. 2017;140:3329-3345. doi:10.1093/brain/awx254
Wang J, Redmond SJ, Bertoux M, Hodges JR, Hornberger M. A comparison of magnetic resonance imaging and neuropsychological examination in the diagnostic distinction of Alzheimer's disease and behavioral variant frontotemporal dementia. Front Aging Neurosci. 2016;8:119. doi:10.3389/fnagi.2016.00119
Kloppel S. Brain morphometry and functional imaging techniques in dementia: methods, findings and relevance in forensic neurology. Curr Opin Neurol. 2009;22:612-616.
Bruun M, Rhodius-Meester HFM, Koikkalainen J, et al. Evaluating combinations of diagnostic tests to discriminate different dementia types. Alzheimers Dement Amst Neth. 2018;10:509-518. doi:10.1016/j.dadm.2018.07.003
Houmani N, Vialatte F, Gallego-Jutglà E, et al. Diagnosis of Alzheimer's disease with electroencephalography in a differential framework. PloS One. 2018;13:e0193607. doi:10.1371/journal.pone.0193607
Koikkalainen J, Rhodius-Meester H, Tolonen A, et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage Clin. 2016;11:435-449. doi:10.1016/j.nicl.2016.02.019
Oppedal K, Engan K, Eftestøl T, Beyer M, Aarsland D. Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images. Biomed Signal Process Control. 2017;33:19-29. doi:10.1016/j.bspc.2016.10.007
Ritter K, Lange C, Weygandt M, et al. Combination of structural MRI and FDG-PET of the brain improves diagnostic accuracy in newly manifested cognitive impairment in geriatric inpatients. J Alzheimers Dis JAD. 2016;54:1319-1331. doi:10.3233/JAD-160380
Myszczynska MA, Ojamies PN, Lacoste AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16:440-456. doi:10.1038/s41582-020-0377-8
Poldrack RA, Baker CI, Durnez J, et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci. 2017;18:115-126. doi:10.1038/nrn.2016.167
Van Essen DC, Smith SM, Barch DM, et al. The WU-Minn Human Connectome Project: an overview. NeuroImage. 2013;80:62-79. doi:10.1016/j.neuroimage.2013.05.041
Nichols TE, Das S, Eickhoff SB, et al. Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci. 2017;20:299-303. doi:10.1038/nn.4500
Pernet C, Garrido MI, Gramfort A, et al. Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci. 2020;23:1473-1483. doi:10.1038/s41593-020-00709-0
Sullivan I, DeHaven A, Mellor D. Open and reproducible research on open science framework. Curr Protoc Essent Lab Tech. 2019;18:e32. doi:10.1002/cpet.32
Gentili C, Cecchetti L, Handjaras G, Lettieri G, Cristea IA. The case for preregistering all region of interest (ROI) analyses in neuroimaging research. Eur J Neurosci. 2021;53:357-361. doi:10.1111/ejn.14954
Hildebrandt M. Preregistration of machine learning research design. Against P-Hacking. PROFILEDCOGITAS SUM COGITAS SUM 10 Years Profiling Eur. Citiz. Amsterdam University Press; 2018:102-105. doi:10.1515/9789048550180-019
Dunne RA, Aarsland D, O'Brien JT, et al. Mild cognitive impairment: the manchester consensus. Age Ageing. 2021;50:72-80. doi:10.1093/ageing/afaa228
Beekly DL, Ramos EM, van Belle G, et al. The national Alzheimer's coordinating center (NACC) database: an Alzheimer disease database. Alzheimer Dis Assoc Disord. 2004;18:270-277.
Sørensen L, Nielsen M, Alzheimer's Disease Neuroimaging Initiative. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. J Neurosci Methods. 2018;302:66-74. doi:10.1016/j.jneumeth.2018.01.003
Qiu S, Joshi PS, Miller MI, et al. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain J Neurol. 2020;143:1920-1933. doi:10.1093/brain/awaa137
Mendelson AF, Zuluaga MA, Lorenzi M, Hutton BF, Ourselin S. Selection bias in the reported performances of AD classification pipelines. NeuroImage Clin. 2017;14:400-416. doi:10.1016/j.nicl.2016.12.018
Sun W, Nasraoui O, Shafto P. Evolution and impact of bias in human and machine learning algorithm interaction. PLOS ONE. 2020;15:e0235502. doi:10.1371/journal.pone.0235502
Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA. 2019;322:2377-2378. doi:10.1001/jama.2019.18058
Williams DR. Miles to go before we sleep: racial inequities in health. J Health Soc Behav. 2012;53:279-295. doi:10.1177/0022146512455804
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447-453. doi:10.1126/science.aax2342
Razai MS, Kankam HKN, Majeed A, Esmail A, Williams DR. Mitigating ethnic disparities in covid-19 and beyond. BMJ. 2021;372:m4921. doi:10.1136/bmj.m4921
Mukadam N, Cooper C, Livingston G. A systematic review of ethnicity and pathways to care in dementia. Int J Geriatr Psychiatry. 2011;26:12-20. doi:10.1002/gps.2484
Iwatsubo T, Iwata A, Suzuki K, et al. Japanese and North American Alzheimer's disease neuroimaging initiative studies: harmonization for international trials. Alzheimers Dement. 2018;14:1077-1087. doi:10.1016/j.jalz.2018.03.009
Lee J, Banerjee J, Khobragade PY, Angrisani M, Dey AB. LASI-DAD study: a protocol for a prospective cohort study of late-life cognition and dementia in India. BMJ Open. 2019;9:e030300. doi:10.1136/bmjopen-2019-030300
Rohrer JD, Nicholas JM, Cash DM, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 2015;14:253-262. doi:10.1016/S1474-4422(14)70324-2
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54:115:1-115:35. doi:10.1145/3457607
A Geometric Solution to Fair Representations | Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society n.d. (accessed April 4, 2023). https://dl.acm.org/doi/abs/10.1145/3375627.3375864
Bellamy RKE, Dey K, Hind M, et al. AI Fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J Res Dev. 2019;63:4:1-4:15. doi:10.1147/JRD.2019.2942287
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artif Intell Healthc. 2020:25-60. doi:10.1016/B978-0-12-818438-7.00002-2
Obermeyer Z, Emanuel EJ. Predicting the future - Big Data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216-1219. doi:10.1056/NEJMp1606181
Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719-731. doi:10.1038/s41551-018-0305-z
Lapointe L, Rivard S. A multilevel model of resistance to information technology implementation. MIS Q. 2005;29:461-491. doi:10.2307/25148692
Liberati EG, Ruggiero F, Galuppo L, et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci IS. 2017;12:113. doi:10.1186/s13012-017-0644-2
Antoniadi AM, Du Y, Guendouz Y, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl Sci. 2021;11:5088. doi:10.3390/app11115088
McDermid JA, Jia Y, Porter Z, Habli I. Artificial intelligence explainability: the technical and ethical dimensions. Philos Transact A Math Phys Eng Sci. 2021;379:20200363. doi:10.1098/rsta.2020.0363
Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc. 2020:295-336. doi:10.1016/B978-0-12-818438-7.00012-5
Jamjoom AAB, Jamjoom AMA, Thomas JP, et al. Autonomous surgical robotic systems and the liability dilemma. Front Surg. 2022;9:1015367. doi:10.3389/fsurg.2022.1015367
Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021;3:e599-611. doi:10.1016/S2589-7500(21)00132-1