scholarly journals Features and Machine Learning Classification of Connected Speech Samples from Patients with Autopsy Proven Alzheimer's Disease with and without Additional Vascular Pathology

2014 ◽  
Vol 42 (s3) ◽  
pp. S3-S17 ◽  
Author(s):  
Vassiliki Rentoumi ◽  
Ladan Raoufian ◽  
Samrah Ahmed ◽  
Celeste A. de Jager ◽  
Peter Garrard
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Pathology ◽  
Connected Speech ◽  
Machine Learning Classification

scholarly journals Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

P2-089: Machine-learning classification of MR scans in Alzheimer's disease based on tensor-based morphometry

2013 ◽  
Vol 9 ◽  
pp. P375-P375
Author(s):  
Motonobu Fujishima ◽  
Norihide Maikusa ◽  
Noriko Chida ◽  
Hiroshi Matsuda ◽  
Fumio Yamashita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Classification

Classification of Alzheimer’s Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation

2020 ◽  
Vol 75 (4) ◽  
pp. 1253-1261 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Francesca Alù ◽  
Matteo Menna ◽  
Elda Judica ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Physiological Aging

scholarly journals Rapid Biomarker Screening of Alzheimer's Disease by Machine Learning and Graphene-Assisted Raman Spectroscopy

2021 ◽  
Author(s):  
Ziyang Wang ◽  
Jiarong Ye ◽  
Li Ding ◽  
Tomotaroh Granzier-Nakajima ◽  
Shubhang Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Raman Spectroscopy ◽  
Brain Slices ◽  
Rapid Screening ◽  
Monolayer Graphene ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
The Brain

As the most common cause of dementia, Alzheimer's disease (AD) faces challenges in terms of understanding of pathogenesis, developing early diagnosis and developing effective treatment. Rapid and accurate identification of AD biomarkers in the brain will be critical to provide novel insights of AD. To this end, in the current work, we developed a system that can enable a rapid screening of AD biomarkers by employing Raman spectroscopy and machine learning analyses in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD, and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, we achieved significantly increased accuracy from 77% to 98% in machine learning classification. Further, we identified the Raman signature bands that are most important in classifying AD and non-AD samples. Based on these, we managed to identify AD-related biomolecules, which have been confirmed by biochemical studies. Our Raman-machine learning integrated method is promising to greatly accelerate the study of AD and can be potentially extended to human samples and various other diseases.

Classification of Alzheimer’s disease based on brain MRI and machine learning

2019 ◽  
Vol 32 (7) ◽  
pp. 1927-1936 ◽  
Author(s):  
Zhao Fan ◽  
Fanyu Xu ◽  
Xuedan Qi ◽  
Cai Li ◽  
Lili Yao
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Mri
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