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A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease [article]

Simeon Spasov, Luca Passamonti, Andrea Duggento, Pietro Lio, Nicola Toschi
2018 bioRxiv   pre-print
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

Ping Zhou, Rong Zeng, Lun Yu, Yabo Feng, Chuxin Chen, Fang Li, Yang Liu, Yanhui Huang, Zhongxiong Huang, the Alzheimer's Disease Neuroimaging Initiative
2021 Frontiers in Aging Neuroscience  
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

Danilo Pena, Jessika Suescun, Mya Schiess, Timothy M. Ellmore, Luca Giancardo, the Alzheimer's Disease Neuroimaging Initiative
2022 Frontiers in Neuroscience  
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]

Yaroslav Shmulev, Mikhail Belyaev
2018 Lecture Notes in Computer Science  
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]

Hongyoon Choi, Kyong Hwan Jin
2017 arXiv   pre-print
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

Ms. Harsimran Guram
2021 Turkish Journal of Computer and Mathematics Education  
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

Weiming Lin, Qinquan Gao, Jiangnan Yuan, Zhiying Chen, Chenwei Feng, Weisheng Chen, Min Du, Tong Tong
2020 Frontiers in Aging Neuroscience  
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

Sijan S Rana, Xinhui Ma, Wei Pang, Emma Wolverson
2020 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)  
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

Kavya M K, Geetha M
2022 International Journal for Research in Applied Science and Engineering Technology  
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

Valeria Diaz, Guillermo Rodríguez
2022 Acta Polytechnica Hungarica  
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  
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

Silvia Basaia, Federica Agosta, Luca Wagner, Elisa Canu, Giuseppe Magnani, Roberto Santangelo, Massimo Filippi
2019 NeuroImage: Clinical  
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

Shikha Agrawal, Neha Sunil Pandharkar, Pooja Arvind Khandelwal, Pratiksha Ashok Pandhare, Janhavi Sanjay Deoghare
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
, 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

Nour ElZawawi, Heba Saber, M Hashem, Tarek Gharib
2022 International Journal of Intelligent Computing and Information Sciences  
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

Min Ju Kang, Sang Yun Kim, Duk L. Na, Byeong C. Kim, Dong Won Yang, Eun-Joo Kim, Hae Ri Na, Hyun Jeong Han, Jae-Hong Lee, Jong Hun Kim, Kee Hyung Park, Kyung Won Park (+12 others)
2019 BMC Medical Informatics and Decision Making  
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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