AIMC Topic: Alzheimer Disease
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Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.
Scientific reports
Aug 30, 2022
In Alzheimer's disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation of intracellular tau accumulation is poorly understood. We employed a high-resolution PET scanner, with low detection thresholds, to examine the Aβ-ta...
Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.
PLoS computational biology
Aug 11, 2022
Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual...
Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.
IEEE transactions on neural networks and learning systems
Aug 3, 2022
Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer's disease (AD)) and forecast its progression. E...
Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.
Annals of nuclear medicine
Aug 1, 2022
OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with susp...
Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.
BMC medical informatics and decision making
Jul 7, 2022
BACKGROUND: Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and interventions. Analyzing electronic health records (EHRs) of patients with AD can help us better und...
Multimodal deep learning for Alzheimer's disease dementia assessment.
Nature communications
Jun 20, 2022
Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we...
Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.
Scientific reports
Jun 15, 2022
Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil light reflex (PLR) for focal red and blue light stimuli in central and peripheral retina in 125 cogniti...
Deep learning for Alzheimer's disease diagnosis: A survey.
Artificial intelligence in medicine
Jun 12, 2022
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle chang...
A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 6, 2022
INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.
Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jun 2, 2022
PURPOSE: To validate our previously proposed method of quantifying amyloid-beta (Aβ) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [F]Florbetapir scans from longitudinal and multicenter ADNI data.
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