Geriatrics

Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis.

Artificial intelligence (AI) is a potentially reliable assistant in the diagnosis of osteoporosis. This meta-analysis aims to assess the diagnostic accuracy of the AI-based systems using medical images. We searched PubMed and Web of Science from inception to June 15, 2020, for eligible articles that applied AI approaches to diagnosing osteoporosis using medical images. Quality and bias of the incl...

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study.

BACKGROUND: Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging.

End-to-end novel visual categories learning via auxiliary self-supervision.

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have ...

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detec...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in...

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches.

BACKGROUND: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. R...

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.

PURPOSE: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been wid...

Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle.

Infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows; therefore, ...

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer's Disease Early Discovery.

Alzheimer's disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning ...

A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.

. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first ...

Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis.

BACKGROUND: Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disabil...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review protocol.

BACKGROUND: Psychosocial health issues such as depression and social isolation are an important cause of morbidity and premature mortality for older a...

Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract.

Accurate lesion segmentation based on endoscopy images is a fundamental task for the automated diagnosis of gastrointestinal tract (GI Tract) diseases...

An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments assoc...

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, s...

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining.

Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase...

Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs.

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroim...

Comprehensive Review of Vision-Based Fall Detection Systems.

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new resea...

Calibration of the EBT3 Gafchromic Film Using HNN Deep Learning.

To achieve a dose distribution conformal to the target volume while sparing normal tissues, intensity modulation with steep dose gradient is used for ...