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A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
[article]
2018
bioRxiv
pre-print
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Some forms of mild cognitive impairment (MCI) can be the clinical precursor of severe dementia like Alzheimers disease (AD), while other types of MCI tend to remain stable over-time and do not progress ...
The most novel characteristics of our machine learning model compared to previous ones are as follows: 1) multi-tasking, in the sense that our deep learning model jointly learns to simultaneously predict ...
ADNI was formed as a multicenter longitudinal study to identify imaging, clinical, genetic and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD) and Mild Cognitive ...
doi:10.1101/383687
fatcat:n5whvglxj5ajpptt5i7o2cvzeq
Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging
2021
Frontiers in Aging Neuroscience
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Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. ...
combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.Methods:18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging ...
., Duggento, A., Liò, P., and Toschi, N. (2019a). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. ...
doi:10.3389/fnagi.2021.764872
pmid:34764864
pmcid:PMC8576572
fatcat:hn2qtpyc4jf2pdn23mjvl3zlcy
Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion
2022
Frontiers in Neuroscience
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Alzheimer's disease (AD) is a progressive neurodegenerative disorder. ...
People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. ...
., Duggento, A., Liò, P., and Toschi, N. (2019). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. ...
doi:10.3389/fnins.2021.744190
pmid:35046766
pmcid:PMC8761739
fatcat:nkkx7gxq75dufbfxxf2xyolmtm
Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging
[chapter]
2018
Lecture Notes in Computer Science
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Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. ...
The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. ...
Conclusion In this work, a problem of conversion prediction from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) was considered. ...
doi:10.1007/978-3-030-00689-1_9
fatcat:i5wr7f3wfzhplb7azgt4ktfkpa
Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging
[article]
2017
arXiv
pre-print
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Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir ...
These results show the feasibility of deep learning as a tool for predicting disease outcome using brain images. ...
Acknowledgement Data collection and sharing for this project was funded by the Alzheimer's Disease
Disclosure None declared. ...
arXiv:1704.06033v1
fatcat:4mp4f53qibdrniothvrvg5n3ua
Improved Demntia Images Detection And Classification Using Transfer Learning Base Convulation Mapping With Attention Layer And XGBOOST Classifier
2021
Turkish Journal of Computer and Mathematics Education
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Then, attention-based, transfer learning (Extracting variable characteristics of patterns from MRI scans) was used to generate more accurate predictive patterns, and finally, features were trained and ...
A classification scheme for the etiology of brain disease based on magnetic resonance imaging is proposed in this paper. ...
, conversion to Mild cognitive impairment and to Alzheimer's disease . [3]
2019
proposed a system in which Speech recording is done of the patient. ...
doi:10.17762/turcomat.v12i6.1293
fatcat:lhuxez27afcihlaq7cldqcopua
Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data
2020
Frontiers in Aging Neuroscience
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Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer's disease (AD) is crucial for early treatment of AD. ...
This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. ...
ACKNOWLEDGMENTS The ADNI data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; NIH grant U01 AG024904 ...
doi:10.3389/fnagi.2020.00077
pmid:32296326
pmcid:PMC7140986
fatcat:kn2sr4iv5vgoldsdig5ayuy4ta
A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease
2020
2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
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We proposed a model, MudNet, to utilise deep learning in the simultaneous prediction of progressive/stable MCI classes and time-to-AD conversion where high-risk pMCI people see conversion to AD within ...
Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease -many people however remain stable or even demonstrate improvement in cognition. ...
DISCUSSION In this paper, a convolutional neural network -MudNet is developed to discriminate mild cognitively impaired people who convert to Alzheimer's Disease, from those who stabilised at the condition ...
doi:10.1109/bdcat50828.2020.00013
fatcat:sgjedbe7jbetzpgmnota4rqycm
Prediction of Alzheimer\'s Disease Using CNN
2022
International Journal for Research in Applied Science and Engineering Technology
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We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. ...
Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. ...
that learns from a small dataset and still demonstrates superior performance for Alzheimer Disease diagnosis. 3) To develop an efficient approach to training a deep learning model with an balanced dataset ...
doi:10.22214/ijraset.2022.45357
fatcat:rsq3rnhftrdxbgujoa2jx4fjiu
Machine Learning for Detection of Cognitive Impairment
2022
Acta Polytechnica Hungarica
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The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. ...
decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). ...
Deep Learning Despite the fact that numerous studies have lately used machine learning approaches to diagnose Alzheimer's disease, most existing studies have found a bottleneck in diagnosis performance ...
doi:10.12700/aph.19.5.2022.5.10
fatcat:pzyv7o4wnfd4ljbyvkb4xo4l4e
Detecting Alzheimer's Disease Using Brain MRI
2022
Zenodo
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This article reviews some of the most recent research on Alzheimer's disease and discusses how machine learning (ML), deep learning (DL), and other brain imaging techniques can help with an earlier identification ...
People of all ages are susceptible to the dementia known as Alzheimer's disease (AD). ...
The suggested International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 approach successfully distinguished Alzheimer's disease from mild cognitive impairment and other types ...
doi:10.5281/zenodo.6965356
fatcat:zrvhmh7kkvbydogqih7x55c3f4
Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks
2019
NeuroImage: Clinical
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We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional ...
CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. ...
Funding Acknowledgements 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 ...
doi:10.1016/j.nicl.2018.101645
pmid:30584016
pmcid:PMC6413333
fatcat:vpl6w743vfdwbekpvewny7houe
Alzheimer's Disease Classification Using Deep CNN
2021
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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, i.e. mild cognitive impairment (MCI) or earlier steps. ...
Especially in the world, the deep learning algorithm has become a technique of choice for analyzing medical images rapidly. ...
The comparison section shows the diagnosis of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Elderly Normal Control (NC) with newly developed algorithms accomplished high accuracy. ...
doi:10.32628/cseit217371
fatcat:6tgqbmj2ordkrmytqgetycyp4e
An efficient Hybrid approach for diagnosis High dimensional data for Alzheimer's diseases Using Machine Learning algorithms
2022
International Journal of Intelligent Computing and Information Sciences
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Alzheimer's disease (AD) is the most familiar type of dementia, a well-known term for memory loss and other cognitive disabilities. The disease is dangerous enough to interfere with ordinary life. ...
The fundamental reason behind this work is to support geriatricians diagnose AD; by creating a clinically translatable machine learning approach. ...
Building and validating Convolutional neural networks (CNNs) to predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single ...
doi:10.21608/ijicis.2022.116420.1153
fatcat:sfg6ria6e5fy7n6bgqinxkwjr4
Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data
2019
BMC Medical Informatics and Decision Making
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To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. ...
Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. ...
Background Cognitive impairment is a spectrum that ranges from subjective cognitive decline to mild cognitive impairment (MCI) andat its enddementia [1] . ...
doi:10.1186/s12911-019-0974-x
pmid:31752864
pmcid:PMC6873409
fatcat:h2lpcikmubbqboppkikynqbd4q
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