Geriatrics

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current stand...

End-to-end deep learning for recognition of ploidy status using time-lapse videos.

PURPOSE: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to a...

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.

Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount...

Hidden Fluids in Plain Sight: Identifying Intravenous Medication Classes as Contributors to Intensive Care Unit Fluid Intake.

Fluid stewardship targets optimal fluid management to improve patient outcomes. Intravenous (IV) medications, flushes, and blood products, collective...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be c...

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.

The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. Howev...

Force-Free Control for Direct Teaching of a Surgical Assistant Robot End Effector with Wire-Driven Bidirectional Telescopic Mechanism.

This paper shows the design and modeling of an end effector with a bidirectional telescopic mechanism to allow a surgical assistant robot to hold and ...

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

OBJECTIVES: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India.

Deep recurrent model for individualized prediction of Alzheimer's disease progression.

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatmen...

Home-care robots - Attitudes and perceptions among older people, carers and care professionals in Ireland: A questionnaire study.

Many countries face major challenges to ensure that their health and social care systems are ready for the growing numbers of older people (OP). As a ...

Simultaneous imputation and classification using Multigraph Geometric Matrix Completion (MGMC): Application to neurodegenerative disease classification.

Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve a...

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.

Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predictin...

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.

Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (...

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the...

White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging pr...

Pharm-AutoML: An open-source, end-to-end automated machine learning package for clinical outcome prediction.

Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorit...

A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors.

People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavio...

Humanoid socially assistive robots in dementia care: a qualitative study about expectations of caregivers and dementia trainers.

OBJECTIVE: To examine the expectations of informal caregivers, nurses, and dementia trainers regarding the support of (physical and psychosocial) huma...

Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN).

The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerati...