Geriatrics

Biological constraints on neural network models of cognitive function.

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is ...

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular mutation is less well-characterized. We aimed to investigate ...

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding.

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking plac...

Laparotomic versus robotic surgery in elderly patients with endometrial cancer: A systematic review and meta-analysis.

BACKGROUND: Although robotics has been shown to improve outcomes in some high-difficulty surgical category patients, it is unclear if such an approach...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairme...

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study.

BACKGROUND: Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fa...

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the est...

Needs and Preferences of Middle-Aged and Older Adults in Taiwan for Companion Robots and Pets: Survey Study.

BACKGROUND: In recent years, robots have been considered a new tech industry that can be used to solve the shortage in human resources in the field of...

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. W...

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

BACKGROUND: Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with...

XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adul...

End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA.

PURPOSE: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing ...

Ensemble machine learning of factors influencing COVID-19 across US counties.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It...

Deep Learning-Based End-to-End Diagnosis System for Avascular Necrosis of Femoral Head.

As the first diagnostic imaging modality of avascular necrosis of the femoral head (AVNFH), accurately staging AVNFH from a plain radiograph is critic...

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI...

High Accuracy WiFi-Based Human Activity Classification System with Time-Frequency Diagram CNN Method for Different Places.

Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitabl...

Validity of robotic simulation for high-stakes examination: a pilot study.

Simulation is increasingly being used to train surgeons and access technical competency in robotic skills. The construct validity of using simulation ...

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network.

BACKGROUND: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establis...

Internet of Things and Robotics in Transforming Current-Day Healthcare Services.

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in h...

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinic...