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Cognitive Dysfunction

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Multimodal deep learning for Alzheimer's disease dementia assessment.

Nature communications
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...

Neuropsychological test using machine learning for cognitive impairment screening.

Applied neuropsychology. Adult
OBJECTIVES: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models tha...

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)
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.

The perspectives of older adults with mild cognitive impairment and their caregivers on the use of socially assistive robots in healthcare: exploring factors that influence attitude in a pre-implementation stage.

Disability and rehabilitation. Assistive technology
BACKGROUND: Due to increasing age and an increasing prevalence rate of neurocognitive disorders such as Mild Cognitive Impairment (MCI) and dementia, independent living may become challenging. The use of socially assistive robots (SARs) is one soluti...

Early diagnosis of Alzheimer's disease based on deep learning: A systematic review.

Computers in biology and medicine
BACKGROUND: The improvement of health indicators and life expectancy, especially in developed countries, has led to population growth and increased age-related diseases, including Alzheimer's disease (AD). Thus, the early detection of AD is valuable ...

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

IEEE journal of biomedical and health informatics
Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to a...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Neuroscience
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic meth...

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Human brain mapping
Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus,...

Analysis of Factors Affecting Cranial Nerve Function of Patients With Vascular Mild Cognitive Impairment Through Functional Magnetic Resonance Imaging Under Artificial Intelligence Environment.

Frontiers in public health
The study aimed to explore the risk factors of effects of patients with vascular mild cognitive impairment (VaMCI) through functional magnetic resonance imaging (fMRI). In this study, 62 patients were selected from the department of neurology, admitt...