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Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

Ninon Burgos, Simona Bottani, Marie-Odile Habert, Theodoros Evgeniou, Stephane Epelbaum, Olivier Colliot, Jorge Samper-Gonzalez, Bennett A. Landman, Elsa D. Angelini
2019 Medical Imaging 2019: Image Processing  
Various machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer's disease (AD) using neuroimaging data.  ...  Finally, combining clinical and neuroimaging data, prediction results further improved to 79% balanced accuracy and an AUC of 0.89).  ...  In conclusion, we proposed a reproducible framework for evaluation of methods for predicting progression to AD.  ... 
doi:10.1117/12.2512430 dblp:conf/miip/Samper-Gonzalez19 fatcat:yny5cb7c6zdovmmcgdum3hmbqm

A Review on Image- and Network-based Brain Data Analysis Techniques for Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis [article]

Mayssa Soussia, Islem Rekik
2018 arXiv   pre-print
However, very few works aimed to predict MCI progression based on early neuroimaging-based observations.  ...  In this study, we reviewed neuroimaging-based technical methods developed for AD and mild-cognitive impairment (MCI) classification and prediction tasks, selected by screening all MICCAI proceedings published  ...  Hence, they become more comparable and easier to depict their relationship in order to predict clinical scores of Alzheimer's disease.  ... 
arXiv:1808.01951v1 fatcat:zj2p6w5xw5abbdfhhs6ubfzz74

Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review [article]

Manon Ansart, Stephane Epelbaum, Giulia Bassignana, Alexandre Bone, Simona Bottani, Tiziana Cattai, Raphael Couronne, Johann Faouzi, Igor Koval, Maxime Louis, Elina Thibeau-Sutre, Junhao Wen (+5 others)
2020 biorxiv/medrxiv   pre-print
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the  ...  The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments.  ...  Data used in preparation of this article were obtained from the Alzheimer's Dis-  ... 
doi:10.1101/2020.09.01.20185959 fatcat:f5vrfriotrf7bn63ovcylv7rue

Random forest prediction of Alzheimer's disease using pairwise selection from time series data

P. J. Moore, T. J. Lyons, J. Gallacher, Stephen D Ginsberg
2019 PLoS ONE  
To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input  ...  While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring  ...  Acknowledgments Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).  ... 
doi:10.1371/journal.pone.0211558 pmid:30763336 pmcid:PMC6375557 fatcat:eln2nnds6vhzdlvw3ionobkclm

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

Gemma Lombardi, Giada Crescioli, Enrica Cavedo, Ersilia Lucenteforte, Giovanni Casazza, Alessandro-Giacco Bellatorre, Chiara Lista, Giorgio Costantino, Giovanni Frisoni, Gianni Virgili, Graziella Filippini, Cochrane Dementia and Cognitive Improvement Group
2020 The Cochrane library  
In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10%  ...  We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not.  ...  Visual evaluation of medial temporal lobe atrophy as a clinical marker of conversion from mild cognitive impairment to dementia and for predicting progression in patients with mild cognitive impairment  ... 
doi:10.1002/14651858.cd009628.pub2 pmid:32119112 pmcid:PMC7059964 fatcat:3rtrfxunsbgtreppsu3u36k5wm

Ten years of image analysis and machine learning competitions in dementia [article]

Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby
2022 arXiv   pre-print
data beyond the Alzheimer's Disease Neuroimaging Initiative.  ...  Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk  ...  ., 2022) What are the data, features and approaches that are the most predictive of future progression of subjects at risk of Alzheimer's disease to aid identification of patients for inclusion in clinical  ... 
arXiv:2112.07922v2 fatcat:tfexsjejife3vav4ywx3nmifdy

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane
2020 International Journal of Molecular Sciences  
together with multi-omics and neuroimaging data.  ...  diagnosis prediction, and the detection of potential biomarkers.  ...  For instance, in order to discriminate normal aging from mild to severe sporadic Alzheimer's disease, Kloppel et al.  ... 
doi:10.3390/ijms21030969 pmid:32024055 pmcid:PMC7037937 fatcat:wc3cjt6dxncjlhyilafo743lvy

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review [article]

Robin J Borchert, Tiago Azevedo, Amanpreet Badhwar, Jose Bernal, Matthew Betts, Rose Bruffaerts, Michael C Burkhart, Ilse Dewachter, Helena Gellersen, Audrey Low, Luiza Machado, Christopher R Madan (+18 others)
2021 medRxiv   pre-print
Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases.  ...  After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results.  ...  There is some progress to address methodological heterogeneity within the broader neuroimaging field, such as the introduction of a reproducible classification protocol for MRI and PET data from the ADNI  ... 
doi:10.1101/2021.12.12.21267677 fatcat:44rogdmirrg35jcp66bukd54dq

MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset

Ian B. Malone, David Cash, Gerard R. Ridgway, David G. MacManus, Sebastien Ourselin, Nick C. Fox, Jonathan M. Schott
2013 NeuroImage  
Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and  ...  The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild-moderate Alzheimer's subjects and 23 controls.  ...  The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline and funding from the UK Alzheimer's Society (to Dr Schott) We are grateful to the funders for their  ... 
doi:10.1016/j.neuroimage.2012.12.044 pmid:23274184 pmcid:PMC3809512 fatcat:h24nmnr3qvhtlf7wi5mrht4wti

Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data [article]

Taeho Jo, Kwangsik Nho, Andrew J. Saykin
2019 arXiv   pre-print
The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has  ...  up to 96.0% for AD classification and 84.2% for MCI conversion prediction.  ...  In a limited available neuroimaging data set, hybrid methods have produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from MCI to AD; deep learning approaches  ... 
arXiv:1905.00931v3 fatcat:pxe3w4ebazaorfsavgrx5gop6e

Structural connectivity centrality changes mark the path toward Alzheimer's disease

Luis R. Peraza, Antonio Díaz-Parra, Oliver Kennion, David Moratal, John-Paul Taylor, Marcus Kaiser, Roman Bauer
2019 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis.  ...  The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques  ...  Acknowledgments The authors would like to thank Peter N. Taylor and Yujiang Wang for their stimulating feedback and suggestions. Funding: A.D.-P. was supported by grant FPU13/01475  ... 
doi:10.1016/j.dadm.2018.12.004 pmid:30723773 pmcid:PMC6350419 fatcat:2aikbg6arfag7bgsjrowc3cidm

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer's disease: diagnosis, longitudinal progress and biological basis

Kun Zhao, Yanhui Ding, Ying Han, Yong Fan, Aaron F. Alexander-Bloch, Tong Han, Dan Jin, Bing Liu, Jie Lu, Chengyuan Song, Pan Wang, Dawei Wang (+12 others)
2020 Science Bulletin  
Further analyses of a large, independent the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings.  ...  However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation  ...  The detailed descriptions of radiomics features was defined by Hugo J.W.L. Aerts and colleagues. We sincerely thank Dr. Tianming Liu and Dr. Feng Shi for kindly helping to prepare this manuscript.  ... 
doi:10.1016/j.scib.2020.04.003 fatcat:gyfmcagbgjao7fsk4d5ywsmlti

Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis

V.P. Subramanyam Rallabandi, Ketki Tulpule, Mahanandeeshwar Gattu
2020 Informatics in Medicine Unlocked  
impairment, and patients with probable Alzheimer's disease who underwent brain imaging were obtained from the Alzheimer's Disease Neuroimaging Initiative database.  ...  and Objective: Early detection of dementia for clinical diagnosis is challenging due to high subjectivity and individual variability in cognitive assessments, as well as the evaluation of protein biomarkers  ...  On the other hand, prediction of the disease progression or conversion from one stage to other needs longitudinal data analysis.  ... 
doi:10.1016/j.imu.2020.100305 fatcat:hn3nyde2cvhqfmcrxceuckambu

Assessing Candidate Serum Biomarkers for Alzheimer's Disease: A Longitudinal Study

Matthew Zabel, Matthew Schrag, Claudius Mueller, Weidong Zhou, Andrew Crofton, Floyd Petersen, April Dickson, Wolff M. Kirsch
2012 Journal of Alzheimer's Disease  
The ability of each marker to predict which subjects with MCI would progress to dementia and which would remain cognitively stable was assessed.  ...  Because of the growing impact of late onset cognitive loss, considerable effort has been directed toward the development of improved diagnostic techniques for Alzheimer's disease (AD) that may pave the  ...  Acknowledgments The authors would like to thank Wayne Kelln for his technical advice. This study was supported in part by the National Institute of Health (AG20948).  ... 
doi:10.3233/jad-2012-112012 pmid:22426016 pmcid:PMC3616608 fatcat:uhpx6plsgrdxbdks6thfnm4shi

A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data

Kan Li, Richard O'Brien, Michael Lutz, Sheng Luo
2018 Alzheimer's & Dementia  
INTRODUCTION-Characterizing progression in Alzheimer's disease (AD) is critically important for early detection and targeted treatment.  ...  It is useful for monitoring disease and identifying patients for clinical trial recruitment.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense  ... 
doi:10.1016/j.jalz.2017.11.004 pmid:29306668 pmcid:PMC5938096 fatcat:bnfoaebrtbccjixseoffbnwr24
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