Geriatrics

Latest AI and machine learning research in geriatrics for healthcare professionals.

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Showing 2584-2604 of 7,277 articles
Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's diseas...

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogene...

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular ...

Hiding a plane with a pixel: examining shape-bias in CNNs and the benefit of building in biological constraints.

When deep convolutional neural networks (CNNs) are trained "end-to-end" on raw data, some of the fea...

Agents and robots for collaborating and supporting physicians in healthcare scenarios.

Monitoring patients through robotics telehealth systems is an interesting scenario where patients' c...

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brai...

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults.

BACKGROUND: Falls in the elderly are a major public health concern because of their high incidence, ...

Prediction of Body Weight of a Person Lying on a Smart Mat in Nonrestraint and Unconsciousness Conditions.

We want to predict body weight while lying in bed for an elderly patient who is unable to move by hi...

Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms.

Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3...

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers.

BACKGROUND: Predictive patient stratification is greatly emerging, because it allows us to prospecti...

Deep learning in interstitial lung disease-how long until daily practice.

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis o...

Significant challenges when introducing care robots in Swedish elder care.

INTRODUCTION: Care robots are machines, operating partly or completely autonomously, that are intend...

Untethered-Bioinspired Quadrupedal Robot Based on Double-Chamber Pre-charged Pneumatic Soft Actuators with Highly Flexible Trunk.

Given that mobile soft robots are adaptable to the environment, they are always tethered with slow l...

Machine-learning-based diagnostics of EEG pathology.

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be cate...

Social Robotics, Education, and Religion in the Islamic World: An Iranian Perspective.

The social impact of robotics applied to domains such as education, religion, nursing, and therapy a...

Amino acid encoding for deep learning applications.

BACKGROUND: The number of applications of deep learning algorithms in bioinformatics is increasing a...

FastSurfer - A fast and accurate deep learning based neuroimaging pipeline.

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimiza...

Robotic assistance for people with dementia: a viable option for the future?

INTRODUCTION: Demographic changes in society and fewer personnel working in healthcare services have...

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey.

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomic...

Fully convolutional attention network for biomedical image segmentation.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (F...

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