دورية أكاديمية
Artificial intelligence for dementia research methods optimization.
العنوان: | Artificial intelligence for dementia research methods optimization. |
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المؤلفون: | Bucholc M; Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK., James C; NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK., Khleifat AA; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK., Badhwar A; Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada.; Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada.; Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada., Clarke N; Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada., Dehsarvi A; Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK., Madan CR; School of Psychology, University of Nottingham, Nottingham, UK., Marzi SJ; UK Dementia Research Institute, Imperial College London, London, UK.; Department of Brain Sciences, Imperial College London, London, UK., Shand C; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK., Schilder BM; UK Dementia Research Institute, Imperial College London, London, UK.; Department of Brain Sciences, Imperial College London, London, UK., Tamburin S; Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy., Tantiangco HM; Information School, University of Sheffield, Sheffield, UK., Lourida I; University of Exeter Medical School, Exeter, UK., Llewellyn DJ; University of Exeter Medical School, Exeter, UK.; The Alan Turing Institute, London, UK., Ranson JM; University of Exeter Medical School, Exeter, UK. |
مؤلفون مشاركون: | Deep Dementia Phenotyping (DEMON) Network; Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK. |
المصدر: | Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2023 Dec; Vol. 19 (12), pp. 5934-5951. Date of Electronic Publication: 2023 Aug 28. |
نوع المنشور: | Journal Article; Review |
اللغة: | English |
بيانات الدورية: | Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE |
أسماء مطبوعة: | Publication: 2020- : Hoboken, NJ : John Wiley & Sons, Ltd. Original Publication: Orlando, FL : Elsevier, Inc. |
مواضيع طبية MeSH: | Artificial Intelligence* , Dementia*/diagnosis, Humans ; Reproducibility of Results ; Machine Learning ; Research Design |
مستخلص: | Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation. (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.) |
التعليقات: | Update of: ArXiv. 2023 Mar 2:arXiv:2303.01949v1.. (PMID: 36911275) |
References: | Patterson C. World Alzheimer report 2018. 2018. Robinson L, Tang E, Taylor J. Dementia: Timely diagnosis and early intervention. BMJ. 2015;350. Pellegrini E, Ballerini L, Maria del C Valdes H, et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimers Dement. 2018;10:519-535. Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H. Neuroimaging and machine learning for dementia diagnosis: Recent advancements and future prospects. IEEE Rev Biomed Eng. 2018;12:19-33. Enshaeifar S, Zoha A, Markides A, et al. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PLoS One. 2018;13(5):e0195605. Mitelpunkt A, Galili T, Kozlovski T, et al. Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy. Sci Rep. 2020;10(1):1-13. Marzi SJ, Schilder B, Nott A, et al. Artificial intelligence for neurodegenerative experimentalmodels. Alzheimers Dement. Accepted. Doherty T, Yao Z, Al Khleifat A, et al. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement. Submitted. Bettencourt C, Skene N, Bandres-Ciga S, et al. Artificial intelligence for dementia genetics and omics. Alzheimers Dement. Submitted. Winchester LM, Harshfield EL, Shi L, et al. Artificial intelligence for alzheimer's disease and associated dementia biomarkers. Alzheimers Dement. Submitted. Borchert RJ, Azevedo T, Badhwar AP, et al. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement. 2023. Portico. doi:10.1002/alz.13412. Newby D, Orgeta V, Marshall CR, et al. Artificial intelligence for dementia prevention. Alzheimers Dement. Submitted. Lyall DM, Kormilitzin A, Lancaster C, et al. Artificial intelligence for dementia-Applied models and digital health. Alzheimers Dement. 2023. Portico. doi:10.1002/alz.13391. Hui HYH, Ran AR, Dai JJ, Cheung CY. Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives. J Alzheimers Dis. 2023;94(1):39-50. Fikry M, Mairittha N, Inoue S. Modelling Reminder System for Dementia by Reinforcement Learning. In Sensor-and Video-Based Activity and Behavior Computing: Proceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021). 2022. Singapore: Springer Nature Singapore. Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: A systematic literature review. JAMIA Open. 2021;4(3):ooab052. Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: A systematic review. Alzheimers Res Ther. 2021;13(1):1-162. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez OL, Yaffe K. Dementia risk indices: A framework for identifying individuals with a high dementia risk. Alzheimers Dement. 2010;6(2):138. Chary E, Amieva H, Pérès K, Orgogozo J, Dartigues J, Jacqmin-Gadda H. Short-versus long-term prediction of dementia among subjects with low and high educational levels. Alzheimers Dement. 2013;9(5):562-571. Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes. 2011;4(1):1-14. Lebedev AV, Westman E, Van Westen G, et al. Random forest ensembles for detection and prediction of alzheimer's disease with a good between-cohort robustness. NeuroImage: Clinical. 2014;6:115-125. Wei W, Visweswaran S, Cooper GF. The application of naive bayes model averaging to predict Alzheimer's disease from genome-wide data. J Am Med Inform Assoc. 2011;18(4):370-375. Kruthika KR, Maheshappa HD. Alzheimer's Disease Neuroimaging Initiative. Multistage classifier-based approach for Alzheimer's disease prediction and retrieval. Inf Med Unlock. 2019;14:34-42. Bucholc M, Ding X, Wang H, et al. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert Syst Appl. 2019;130:157-171. Sharma A, Kaur S, Memon N, Fathima AJ, Ray S, Bhatt MW. Alzheimer's patients detection using support vector machine (SVM) with quantitative analysis. Neurosci Inform. 2021;1(3):100012. Moore PJ, Lyons TJ, Gallacher J, Alzheimer's Disease Neuroimaging Initiative. Random forest prediction of Alzheimer's disease using pairwise selection from time series data. PLoS One. 2019;14(2):e0211558. Lehmann C, Koenig T, Jelic V, et al. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG). J Neurosci Methods. 2007;161(2):342-350. James C, Ranson JM, Everson R, Llewellyn DJ. Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients. JAMA Netw Open. 2021;4(12):e2136553. Cui Y, Liu B, Luo S, et al. Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors. PLoS One. 2011;6(7):e21896. 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. Grimmer T, Wutz C, Alexopoulos P, et al. Visual versus fully automated analyses of 18F-FDG and amyloid PET for prediction of dementia due to Alzheimer disease in mild cognitive impairment. J Nucl Med. 2016;57(2):204-207. Forlenza OV, Radanovic M, Talib LL, et al. Cerebrospinal fluid biomarkers in Alzheimer's disease: Diagnostic accuracy and prediction of dementia. Alzheimers Dement. 2015;1(4):455-463. De Velasco Oriol J, Vallejo EE, Estrada K, Tamez Pena JG. Benchmarking machine learning models for late-onset Alzheimer's disease prediction from genomic data. BMC Bioinf. 2019;20. Korolev IO, Symonds LL, Bozoki AC. Alzheimer's Disease Neuroimaging Initiative. Predicting progression from mild cognitive impairment to Alzheimer's dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PLoS One. 2016;11(2):e0138866. Prakash M, Abdelaziz M, Zhang L, Strange BA, Tohka J. Alzheimer's Disease Neuroimaging Initiative. Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning. J Alzheimer's Dis. 2020(Preprint):1-14. Duchesne S, Caroli A, Geroldi C, Collins DL, Frisoni GB. Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage. 2009;47(4):1363-1370. Duchesne S, Caroli A, Geroldi C, Frisoni GB, Collins DL. Predicting clinical variable from MRI features: application to MMSE in MCI. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2005 Oct 26 (pp. 392-399). Springer, Berlin, Heidelberg. Youssofzadeh V, McGuinness B, Maguire LP, Wong-Lin K. Multi-kernel learning with dartel improves combined MRI-PET classification of Alzheimer's disease in AIBL data: group and individual analyses. Front Hum Neurosci. 2017;11:380. Mofrad SA, Lundervold A, Lundervold AS. Alzheimer's Disease Neuroimaging Initiative. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease. Comput Med Imaging Graphics. 2021;90:101910. Growdon ME, Schultz AP, Dagley AS, et al. Odor identification and Alzheimer disease biomarkers in clinically normal elderly. Neurology. 2015;84(21):2153-2160. Wolters FJ, Zonneveld HI, Hofman A, van der Lugt A, Koudstaal PJ, Vernooij MW. Heart-brain connection collaborative research group. cerebral perfusion and the risk of dementia. Circulation. 2017;136:719-728. Mattsson N, Zetterberg H, Janelidze S, et al. Plasma tau in Alzheimer disease. Neurology. 2016;87(17):1827-1835. Lee S, Viqar F, Zimmerman ME, et al. White matter hyperintensities are a core feature of Alzheimer's disease: Evidence from the dominantly inherited Alzheimer network. Ann Neurol. 2016;79(6):929-939. Irwin DJ, Grossman M, Weintraub D, et al. Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: A retrospective analysis. Lancet Neurol. 2017;16(1):55-65. Asanomi Y, Shigemizu D, Akiyama S, Sakurai T, Ozaki K, Ochiya T, Niida S. Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data. Sci Rep. 2021;11(1):20947. Agbavor F, Liang H. Predicting dementia from spontaneous speech using large language models. PLOS Digital Health. 2022;1(12):e0000168. Yang T, Wang J, Sun Q, et al. Detecting genetic risk factors for Alzheimer's disease in whole genome sequence data via lasso screening. 2015:985-989. Dayon L, Guiraud SP, Corthésy J, et al. One-carbon metabolism, cognitive impairment and CSF measures of Alzheimer pathology: Homocysteine and beyond. Alzheimers Res Ther. 2017;9(1):1-11. Silver M, Janousova E, Hua X, Thompson PM, Montana G. Alzheimer's Disease Neuroimaging Initiative. Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. Neuroimage. 2012;63(3):1681-1694. Teipel SJ, Grothe MJ, Metzger CD, et al. Robust detection of impaired resting state functional connectivity networks in Alzheimer's disease using elastic net regularized regression. Front Aging Neurosci. 2017;8:318. Kaut O, Schmitt I, Tost J, et al. Epigenome-wide DNA methylation analysis in siblings and monozygotic twins discordant for sporadic parkinson's disease revealed different epigenetic patterns in peripheral blood mononuclear cells. Neurogenetics. 2017;18(1):7-22. Bouts MJ, Möller C, Hafkemeijer A, et al. Single subject classification of Alzheimer's disease and behavioral variant frontotemporal dementia using anatomical, diffusion tensor, and resting-state functional magnetic resonance imaging. J Alzheimer's Dis. 2018;62(4):1827-1839. Cleret de Langavant L, Bayen E, Bachoud-Lévi A, Yaffe K. Approximating dementia prevalence in population-based surveys of aging worldwide: An unsupervised machine learning approach. Alzheimers Dement. 2020;6(1):e12074. Ranson JM, Kuźma E, Hamilton W, Muniz-Terrera G, Langa KM, Llewellyn DJ. Predictors of dementia misclassification when using brief cognitive assessments. Neurology: Clinical Practice. 2019;9(2):109-117. Raket LL. Statistical disease progression modeling in Alzheimer disease. Front Big Data. 2020;3. Leoutsakos J, Gross AL, Jones RN, Albert MS, Breitner J. ‘Alzheimer's progression score’: Development of a biomarker summary outcome for AD prevention trials. J Prev Alzheimers Dis. 2016;3(4):229. Lorenzi M, Filippone M, Frisoni GB, Alexander DC, Ourselin S. Alzheimer's Disease Neuroimaging Initiative. Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. Neuroimage. 2019;190:56-68. Firth NC, Primativo S, Brotherhood E, et al. Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression. Alzheimers Dement. 2020;16(7):965-973. Fonteijn HM, Modat M, Clarkson MJ, et al. An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease. Neuroimage. 2012;60(3):1880-1889. Young AL, Oxtoby NP, Daga P, et al. A data-driven model of biomarker changes in sporadic Alzheimer's disease. Brain. 2014;137(9):2564-2577. Eshaghi A, Young AL, Wijeratne PA, et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun. 2021;12(1):1-12. Vogel JW, Young AL, Oxtoby NP, et al. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med. 2021;27(5):871-881. 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(1):1-16. Whitwell JL, Przybelski SA, Weigand SD, et al. Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: A cluster analysis study. Brain. 2009;132(11):2932-2946. Toschi N, Lista S, Baldacci F, et al. Biomarker-guided clustering of Alzheimer's disease clinical syndromes. Neurobiol Aging. 2019;83:42-53. Oxtoby NP, Alexander DC. Imaging plus X: Multimodal models of neurodegenerative disease. Curr Opin Neurol. 2017;30(4):371. Arbelaitz O, Gurrutxaga I, Muguerza J, Pérez JM. Perona I. An extensive comparative study of cluster validity indices. Pattern Recognit. 2013;46(1):243-256. Khanna S, Domingo-Fernández D, Ayyappan A, Emon MA, Hofmann-Apitius M, Fröhlich H. Using multi-scale genetic, neuroimaging and clinical data for predicting alzheimer's disease and reconstruction of relevant biological mechanisms. Sci Rep. 2018;8(1):1-13. Van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learning. 2020;109(2):373-440. An L, Adeli E, Liu M, Zhang J, Shen D. Semi-supervised hierarchical multimodal feature and sample selection for alzheimer's disease diagnosis. 2016:79-87. Batmanghelich KN, Dong HY, Pohl KM, Taskar B, Davatzikos C. Disease classification and prediction via semi-supervised dimensionality reduction. 2011:1086-1090. Filipovych R, Davatzikos C. Alzheimer's Disease Neuroimaging Initiative. Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI). Neuroimage. 2011;55(3):1109-1119. Teramoto R. Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation. Comput Biol Chem. 2008;32(6):438-441. Bengio Y, LeCun Y. Scaling learning algorithms towards AI. Large-Scale Kernel Machines. 2007;34(5):1-41. Morabito FC, Campolo M, Ieracitano C, et al. Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings. 2016:1-6. Spasov S, Passamonti L, Duggento A, Lio P, Toschi N. Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage. 2019;189:276-287. Mahendran N, Vincent P M DR. Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data. Comput Struct Biotechnol J. 2023;21:1651-1660. Li W, Zhao J, Shen C, Zhang J, et al. Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis. Front Neuroinform. 2022;16:886365. Alam R, Anderson M, Bankole A, Lach J. Inferring physical agitation in dementia using smartwatch and sequential behavior models. 2018:170-173. 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. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 2019 Apr 8 (pp. 368-371). IEEE. Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: A survey. Evol Intell. 2021:1-22. Yang Q, Li X, Ding X, Xu F, Ling Z. Deep learning-based speech analysis for Alzheimer's disease detection: A literature review. Alzheimers Res Ther. 2022;14(1):1-6. Baur C, Denner S, Wiestler B, Navab N, Albarqouni S. Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med Image Anal. 2021;69:101952. Bertini F, Allevi D, Lutero G, et al. An automatic Alzheimer's disease classifier based on spontaneous spoken English. Comput Speech Lang. 2022;72:101298. Ithapu VK, Singh V, Okonkwo OC, et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimer Dementia. 2015;11(12):1489-1499. Bandyopadhyay S, Wittmayer J, Libon DJ, Tighe P, Price C, Rashidi P. Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands. Sci Rep. 2023;13(1):7384. Wang Y, Gu X, Hou W, Zhao M, Sun L, Guo C. Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data. Brain Sci. 2023;13(2):306. Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Comput Methods Programs Biomed. 2020;187:105242. Klöppel S, Stonnington CM, Chu C, et al. Automatic classification of MR scans in Alzheimer's disease. Brain. 2008;131(3):681-689. Burgos N, Colliot O. Machine learning for classification and prediction of brain diseases: Recent advances and upcoming challenges. Curr Opin Neurol. 2020;33(4):439-450. Gupta Y, Lama RK, Kwon G, et al. Prediction and classification of Alzheimer's disease based on combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. Front Comput Neurosci. 2019;13:72. Bron EE, Klein S, Reinke A, Papma JM, Maier-Hein L, Alexander DC, Oxtoby NP. Ten years of image analysis and machine learning competitions in dementia. Neuroimage. 2022;253:119083. 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):2924-2935. Bucholc M, Titarenko S, Ding X, Canavan C, Chen T. A hybrid machine learning approach for prediction of conversion from mild cognitive impairment to dementia. Expert Syst Appl. 2023:119541. Lin RH, Wang CC, Tung CW. A machine learning classifier for predicting stable MCI patients using gene biomarkers. Int J Environ Res Public Health. 2022;19(8):4839. Lee G, Nho K, Kang B, Sohn KA, Kim D. Predicting Alzheimer's disease progression using multi-modal deep learning approach. Sci Rep. 2019;9(1):1-2. Ansart M, Epelbaum S, Bassignana G, et al. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Med Image Anal. 2020:101848. Samper-Gonzalez J, Burgos N, Bottani S, Habert MO, Evgeniou T, Epelbaum S, Colliot O. Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, pp. 221-233). SPIE. Agbavor F, Liang H. Predicting dementia from spontaneous speech using large language models. PLOS Digital Health. 2022;1(12):e0000168. Fraser KC, Fors KL, Kokkinakis D. Multilingual word embeddings for the assessment of narrative speech in mild cognitive impairment. Comput Speech Lang. 2019;53:121-139. Vonk JM, Greving JP, Gudnason V, Launer LJ, Geerlings MI. Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study. Eur J Epidemiol. 2021;36(10):1025-1041. Danso SO, Zeng Z, Muniz-Terrera G, Ritchie CW. Developing an explainable machine learning-based personalised dementia risk prediction model: A transfer learning approach with ensemble learning algorithms. Front Big Data. 2021;4:21. Antila K, Lötjönen J, Thurfjell L, et al. The PredictAD project: Development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease. Interface Focus. 2013;3(2):20120072. Bruun M, Frederiksen KS, Rhodius-Meester HF, et al. Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Alzheimer Re s Therapy. 2019;11(1):1-1. Gorse AD. Diversity in medicinal chemistry space. Curr Top Med Chem. 2006;6(1):3-18. David L, Arús-Pous J, Karlsson J, et al. Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research. Front Pharmacol. 2019;10:1303. Lipinski CF, Maltarollo VG, Oliveira PR, da Silva AB, Honorio KM. Advances and perspectives in applying deep learning for drug design and discovery. Frontiers in Robotics and AI. 2019;6:108. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-1358. Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science. 2018;4(2):268-276. Vatansever S, Schlessinger A, Wacker D, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427-1473. Leung MK, Delong A, Alipanahi B, Frey BJ. Machine learning in genomic medicine: A review of computational problems and data sets. Proc IEEE. 2015;104(1):176-197. Dias TL, Schuch V, Beltrão-Braga PCB, et al. Drug repositioning for psychiatric and neurological disorders through a network medicine approach. Translational Psychiatry. 2020;10(1):1-10. Rodriguez S, Hug C, Todorov P, et al. Machine learning identifies candidates for drug repurposing in Alzheimer's disease. Nat Commun. 2021;12(1):1-3. Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50(8):1161-1170. Dey KK, Van de Geijn B, Kim SS, Hormozdiari F, Kelley DR, Price AL. Evaluating the informativeness of deep learning annotations for human complex diseases. Nat Commun. 2020;11(1):1-9. Breiman L. Statistical modeling: The two cultures. Quality Control and Applied Statistics. 2003;48(1):81-82. Navarro E, Udine E, de Paiva Lopes K, et al. Discordant transcriptional signatures of mitochondrial genes in Parkinson's disease human myeloid cells. Biorxiv. 2020. Grubman A, Chew G, Ouyang JF, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22(12):2087-2097. Anderson NC, Chen P, Meganathan K, et al. Balancing serendipity and reproducibility: Pluripotent stem cells as experimental systems for intellectual and developmental disorders. Stem Cell Rep. 2021. Jiang J, Wang C, Qi R, Fu H, Ma Q. scREAD: A single-cell RNA-seq database for Alzheimer's disease. iScience. 2020;23(11):101769. Lotfollahi M, Wolf FA, Theis FJ. scGen predicts single-cell perturbation responses. Nat Methods. 2019;16(8):715-721. Li H, Guan Y. Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution. Genome Res. 2021;31(4):721-731. Quang D, Xie X. FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods. 2019;166:40-47. Health TL. Guiding better design and reporting of AI-intervention trials. The Lancet. Digital Health. 2020;2(10):e493. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577-591. Ezzati A, Lipton RB. Alzheimer's Disease Neuroimaging Initiative. Machine learning predictive models can improve efficacy of clinical trials for Alzheimer's disease. J Alzheimer's Dis. 2020;74(1):55-63. Reith FH, Mormino EC, Zaharchuk G. Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection. Alzheimer Dementia. 2021;7(1):e12212. Hane CA, Nori VS, Crown WH, Sanghavi DM, Bleicher P. Predicting onset of dementia using clinical notes and machine learning: case-control study. JMIR Medical Informatics. 2020;8(6):e17819. Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open. 2021;4(3):ooab052. Wilkinson MD, Dumontier M, Aalbersberg IJ, t al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3(1):1-9. Mueller SG, Weiner MW, Thal LJ, et al. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin N Am. 2005;15(4):869. Arokiasamy P, Bloom D, Lee J, Feeney K, Ozolins M. Longitudinal aging study in india: Vision, design, implementation, and preliminary findings. In: Aging in asia: Findings from new and emerging data initiatives. National Academies Press (US); 2012. Beekly DL, Ramos EM, Lee WW, et al. The National Alzheimer's Coordinating Center (NACC) database: the uniform data set. Alzheimer Dis Assoc Disord. 2007;21(3):249-258. Matthews PM, Sudlow C. The UK biobank. Brain. 2015;138(12):3463-3465. Fukunaga K, Hayes RR. Effects of sample size in classifier design. IEEE Trans Pattern Anal Mach Intell. 1989;11(8):873-885. Martin SA, Townend FJ, Barkhof F, Cole JH. Interpretable machine learning for dementia: A systematic review. Alzheimer Dementia. 2023;19(5): 2135-2149. Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst. 2023;47(1):17. Archana M, Ramakrishnan S. Detection of Alzheimer disease in MR images using structure tensor. In Annu Int Conf IEEE Eng Med Biol Soc. 2014: 1043-1046. Zheng X, Shi J, Li Y, Liu X, Zhang Q. Multi-modality stacked deep polynomial network based feature learning for Alzheimer's disease diagnosis. In 2016 IEEE 13th international symposium on biomedical imaging (ISBI). 2016. 851-854. McEvoy LK, Fennema-Notestine C, Roddey JC, et al. Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology. 2009;251(1):195-205. Pellegrini E, Ballerini L, Hernandez MD, et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer Dementia. 2018;10:519-535. Mueller SG, Weiner MW, Thal LJ, et al. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clinics. 2005;15(4):869-877. Bucholc M, McClean PL, Bauermeister S, et al. Association of the use of hearing aids with the conversion from mild cognitive impairment to dementia and progression of dementia: a longitudinal retrospective study. Alzheimer Dementia. 2021;7(1):e12122. You J, Zhang YR, Wang HF, et al. Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study. EClinicalMedicine. 2022;53. Durongbhan P, Zhao Y, Chen L, et al. A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Trans Neural Syst Rehabil Eng. 2019;27(5):826-835. Khoei TT, Ahajjam MA, Hu WC, Kaabouch N. A Deep Learning Multi-Task Approach for the Detection of Alzheimer's Disease in a Longitudinal Study. In 2022 IEEE International Conference on Electro Information Technology (eIT) 2022 (pp. 315-319). IEEE. Benrimoh D, Israel S, Fratila R, et al. ML and AI safety, effectiveness and explainability in healthcare. Front Big Data. 2021;4. Bruckert S, Finzel B, Schmid U. The next generation of medical decision support: A roadmap toward transparent expert companions. Front Artif Intell Appl. 2020;3:75. Molnar C. Interpretable machine learning. Lulu. com; 2020. Gómez-Ramírez J, Ávila-Villanueva M, Fernández-Blázquez MÁ. Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Sci Rep. 2020;10(1):1-15. Cole J, Wood D, Booth T. Visual attention as a model for interpretable neuroimage classification in dementia: Doctor AI: Making computers explain their decisions. Alzheimer Dementia. 2020;16:e037351. Dyrba M, Pallath AH, Marzban EN. Comparison of cnn visualization methods to aid model interpretability for detecting Alzheimer's disease. In: Bildverarbeitung für die medizin 2020. Springer; 2020:307-312. Qiu S, Joshi PS, Miller MI, et al. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain. 2020;143(6):1920-1933. Ribeiro MT, Singh S, Guestrin C. "Why should i trust you?" explaining the predictions of any classifier. 2016:1135-1144. Shapley LS. 17. A value for n-person games. Princeton University Press; 2016. Spooner A, Chen E, Sowmya A, et al. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci Rep. 2020;10(1):1-10. Bansal D, Chhikara R, Khanna K, Gupta P. Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Computer Science. 2018;132:1497-1502. Williams JA, Weakley A, Cook DJ, Schmitter-Edgecombe M. Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. 2013. Chen R, Herskovits EH. Machine-learning techniques for building a diagnostic model for very mild dementia. Neuroimage. 2010;52(1):234-244. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286(3):800-809. Mårtensson G, Ferreira D, Granberg T, et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study. Med Image Anal. 2020;66:101714. Klöppel S, Peter J, Ludl A, et al. Applying automated MR-based diagnostic methods to the memory clinic: A prospective study. J Alzheimer's Dis. 2015;47(4):939-954. Jun GR, Chung J, Mez J, et al. Transethnic genome-wide scan identifies novel Alzheimer's disease loci. Alzheimers Dement. 2017;13(7):727-738. Kalafatis C, Modarres MH, Apostolou P, et al. Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia. Front Psychiatry. 2021;12:706695. Clarke N, Foltz P, Garrard P. How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease. Cortex. 2020;129:446-463. Fraser KC, Fors KL, Kokkinakis D. Multilingual word embeddings for the assessment of narrative speech in mild cognitive impairment. Comput Speech Lang. 2019;53:121-139. Badhwar A, McFall GP, Sapkota S, et al. A multiomics approach to heterogeneity in Alzheimer's disease: Focused review and roadmap. Brain. 2020;143(5):1315-1331. 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. Chandler C, Foltz PW, Cohen AS, et al. Machine learning for ambulatory applications of neuropsychological testing. Intell Based Med. 2020;1:100006. El-Sappagh S, Alonso JM, Islam SR, Sultan AM, Kwak KS. A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease. Sci Rep. 2021;11(1):1-26. Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJ, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5(9):e2233946. Nation DA, Ho JK, Dutt S, Han SD, Lai MHC; Alzheimer's Disease Neuroimaging Initiative. Neuropsychological Decline Improves Prediction of Dementia Beyond Alzheimer's Disease Biomarker and Mild Cognitive Impairment Diagnoses. J Alzheimers Dis. 2019;69(4):1171-1182. Wen J, Thibeau-Sutre E, Diaz-Melo M, et al. Convolutional neural networks for classification of Alzheimer's disease: overview and reproducible evaluation. Med Image Anal. 2020;63:101694. Gianattasio KZ, Bennett EE, Wei J, et al. Generalizability of findings from a clinical sample to a community-based sample: A comparison of ADNI and ARIC. Alzheimers Dement. 2021;17(8):1265-1276. Steenland K, Macneil J, Bartell S, Lah J. Analyses of diagnostic patterns at 30 Alzheimer's disease centers in the US. Neuroepidemiology. 2010;35(1):19-27. Dodge HH, Zhu J, Woltjer R, et al. Risk of incident clinical diagnosis of Alzheimer's disease-type dementia attributable to pathology-confirmed vascular disease. Alzheimers Dement. 2017;13:613-623. Takahashi M, Idani T, Fukuda H, Kawashima R, Kitamura M. Application of machine learning method to diagnosis of alzheimer diseases using SPECT brain image. In SICE Annual Conference. 2005. Gianattasio KZ, Ciarleglio A, Power MC. Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study. Epidemiology. 2020;31(1):126-133. Mehmood A, Yang S, Feng Z, et al. A transfer learning approach for early diagnosis of Alzheimer's disease on MRI images. Neuroscience. 2021;460:43-52. Richens JG, Lee CM, Johri S. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun. 2020;11(1):1-9. Lecca P. Machine learning for causal inference in biological networks: Perspectives of this challenge. Front bioinform. 2021;1:746712. Luo W, Phung D, Tran T, et al. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016;18(12):e323. |
معلومات مُعتمدة: | RF1 AG055654 United States AG NIA NIH HHS; MR/X005674/1 United Kingdom MRC_ Medical Research Council |
فهرسة مساهمة: | Keywords: artificial intelligence; classification; clinical utility; deep learning; dementia; generalizability; interpretability; machine learning; methods optimization; regression; replicability; semi-supervised learning; supervised learning; transferability; unsupervised learning |
تواريخ الأحداث: | Date Created: 20230828 Date Completed: 20231229 Latest Revision: 20240712 |
رمز التحديث: | 20240713 |
DOI: | 10.1002/alz.13441 |
PMID: | 37639369 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1552-5279 |
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DOI: | 10.1002/alz.13441 |