AIMC Topic: Humans
Showing 65541 to 65550 of 95995 articles
Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome.
Journal of affective disorders
Mar 8, 2019
BACKGROUND: Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to one type of disease. Unveiling the association between depressive and the fatigue diseases provides a...
Beamforming and Speckle Reduction Using Neural Networks.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Mar 8, 2019
With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals...
Light-driven ultrasensitive self-powered cytosensing of circulating tumor cells via integration of biofuel cells and a photoelectrochemical strategy.
Journal of materials chemistry. B
Mar 8, 2019
Herein, a light-driven, membrane-less and mediator-less self-powered cytosensing platform via integration of biofuel cells (BFCs) and a photoelectrochemical strategy was developed for ultrasensitive detection of circulating tumor cells (CTCs). To con...
A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.
European journal of cancer (Oxford, England : 1990)
Mar 8, 2019
BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively ...
Knowledge Domain and Emerging Trends on Echinococcosis Research: A Scientometric Analysis.
International journal of environmental research and public health
Mar 8, 2019
The echinococcosis of humans and animals is a chronic helminthic disease caused by the larva of genus tapeworms. It is a globally distributed disease which is an important socioeconomic and public health problem in many low and middle-income countri...
Deep learning-based super-resolution in coherent imaging systems.
Scientific reports
Mar 8, 2019
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-lim...
NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia.
PloS one
Mar 8, 2019
INTRODUCTION: Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.
Surveys and Questionnaires
Humans
Geriatric Assessment
Machine Learning
Aged
Neuropsychological Tests
Cognitive Dysfunction
Mass Screening
Male
Female
Aged, 80 and over
Dementia
Diagnosis, Differential
Middle Aged
Adult
ROC Curve
Area Under Curve
Diagnosis, Computer-Assisted
Sensitivity and Specificity
Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches.
Health informatics journal
Mar 7, 2019
Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of auton...
Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact.
IEEE journal of biomedical and health informatics
Mar 7, 2019
OBJECTIVE: The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing obj...
CE-Net: Context Encoder Network for 2D Medical Image Segmentation.
IEEE transactions on medical imaging
Mar 7, 2019
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, ...
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