Geriatrics

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

PURPOSE: Computed tomography (CT) is a technique of choice to image bone structure at different scal...

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health...

Neuroprotective and Therapeutic Effects of Tocovid and Twendee-X on Aβ Oligomer-Induced Damage in the SH-SY5Y Cell Line.

BACKGROUND: Alzheimer's disease (AD) is the most frequent cause of dementia among the elderly. The a...

Editorial Comment: Pediatric Sarcopenia-Quest for Diagnosis.

MCG-Net: End-to-End Fine-Grained Delineation and Diagnostic Classification of Cardiac Events From Magnetocardiographs.

In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating...

Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.

Early detection of lung cancer is one way to improve outcomes. Improving the detection of nodules o...

Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment.

AIM: To develop a fully automated deep-learning-based approach to measure muscle area for assessing ...

Adversarial Joint-Learning Recurrent Neural Network for Incomplete Time Series Classification.

Incomplete time series classification (ITSC) is an important issue in time series analysis since tem...

Deep learning based sarcopenia prediction from shear-wave ultrasonographic elastography and gray scale ultrasonography of rectus femoris muscle.

We aim to evaluate the performance of a deep convolutional neural network (DCNN) in predicting the p...

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.

Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability w...

Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI.

This study was to explore the application of MRI based on artificial intelligence technology combine...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral l...

Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating dis...

Visual attention prediction improves performance of autonomous drone racing agents.

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be...

Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

BACKGROUND: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emer...

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Deep learning-based methods have been widely used in medical imaging field such as detection, segmen...

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States fro...

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed ...

Care robot research and development plan for disability and aged care in Korea: A mixed-methods user participation study.

The population of Korea is aging rapidly, and this has led to a care burden for caregivers. Without ...

Prediction of clinical trial enrollment rates.

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor...

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling.

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for ef...