Geriatrics

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier.

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate...

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and ...

From convolutional neural networks to models of higher-level cognition (and back again).

The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being expl...

Speaker recognition based on deep learning: An overview.

Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. H...

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may of...

Cycle consistent network for end-to-end style transfer TTS training.

In this paper, we propose a cycle consistent network based end-to-end TTS for speaking style transfer, including intra-speaker, inter-speaker, and uns...

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection a...

Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.

BACKGROUND: Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatur...

Uncorrelated feature encoding for faster image style transfer.

Recent image style transfer methods use a pre-trained convolutional neural network as their feature encoder. However, the pre-trained network is not o...

Prediction of tau accumulation in prodromal Alzheimer's disease using an ensemble machine learning approach.

We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with pro...

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis.

BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provide...

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current di...

The sTREM2 Concentrations in the Blood: A Marker of Neurodegeneration?

Microglia performs a variety of functions during brain development designed to maintain brain homeostasis. Triggering receptor expressed on myeloid ce...

The concept of justifiable healthcare and how big data can help us to achieve it.

Over the last decades, the face of health care has changed dramatically, with big improvements in what is technically feasible. However, there are ind...

Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.

Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learnin...

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.

BACKGROUND: Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is sile...

Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment.

Smartphones can be used to passively assess and monitor patients' speech impairments caused by ailments such as Parkinson's disease, Traumatic Brain ...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predicti...

Alzheimer's disease detection using depthwise separable convolutional neural networks.

To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision w...

Examining the Utility of the DESCARTE Model for Case Study Research.

The DESCARTE model aims to guide health care researchers on the design, conduct, and reporting of case studies. The current article critically apprais...