Geriatrics

"Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images".

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of CO...

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary glands (SGs), including primary Sjögren's syndrome (pSS). At present, the major obstacle for establishing SUGS as a standardized tool for pSS diagnosis is its low inter/intra observer reliability. The aim of this study was to address this problem by pro...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging ...

Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

OBJECTIVE: To evaluate the accuracy of a deep learning-based auto-segmentation mode to that of manual contouring by one medical resident, where both e...

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensi...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading th...

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. ...

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography.

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautom...

Bone Mineral Density and Content Among Patients With Coronary Artery Disease: A Comparative Study.

INTRODUCTION: Some studies indicate an association between coronary artery disease (CAD) and osteoporosis. This case-control study examined the associ...

A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction.

Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain miss...

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reductio...

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.

BACKGROUND: The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with b...

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for ...

["Puppet accompaniment"-A geragogically based accompaniment approach].

BACKGROUND: Home care for older people with dementia (PwD) is associated with special needs and requires appropriate support services as well as learn...

Dynamic MRI reconstruction with end-to-end motion-guided network.

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of...

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the exi...

Prediction of vascular aging based on smartphone acquired PPG signals.

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is...

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.

BACKGROUND: SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with...

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic perf...

Classification of Aggressive Movements Using Smartwatches.

Recognizing aggressive movements is a challenging task in human activity recognition. Wearable smartwatch technology with machine learning may be a vi...