AIMC Topic: Aged, 80 and over
Clear Filters Showing 2171 to 2180 of 3475 articles
Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning.
NeuroImage
May 20, 2019
Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic a...
Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.
The international journal of cardiovascular imaging
May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Patient Selection
Heart Failure
Cardiac Resynchronization Therapy
Ventricular Remodeling
Image Interpretation, Computer-Assisted
Humans
Electrocardiography
Clinical Decision-Making
Treatment Outcome
Male
Female
Support Vector Machine
Ventricular Function, Left
Aged
Predictive Value of Tests
Echocardiography
Aged, 80 and over
Signal Processing, Computer-Assisted
Middle Aged
Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.
Neuroscience
May 16, 2019
Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) usi...
ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI.
International journal of medical informatics
May 16, 2019
BACKGROUND: Neuroimaging analysis is currently crucial for an early assessment of glioblastoma, to help improving treatment and tumor follow-up. To this end, multiple functional and morphological MRI sequences are usually employed, requiring the deve...
A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.
JACC. Cardiovascular imaging
May 15, 2019
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...
Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
May 15, 2019
OBJECTIVE: The manual adjudication of disease classification is time-consuming, error-prone, and limits scaling to large datasets. In ischemic stroke (IS), subtype classification is critical for management and outcome prediction. This study sought to...
Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Nature communications
May 15, 2019
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept...
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies.
Brachytherapy
May 15, 2019
PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. T...
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.
EBioMedicine
May 14, 2019
BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was dem...
How do "robopets" impact the health and well-being of residents in care homes? A systematic review of qualitative and quantitative evidence.
International journal of older people nursing
May 9, 2019
BACKGROUND: Robopets are small animal-like robots which have the appearance and behavioural characteristics of pets.
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