Geriatrics

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease.

BACKGROUND: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead ...

An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia.

BACKGROUND: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia.

Dermoscopic Features of Giant Molluscum Contagiosum in a Patient with Acquired Immunodeficiency Syndrome.

Giant molluscum contagiosum (MC) is a peculiar variant of the disease with the presence of multiple or single lesions larger than 5 mm. In contrast to...

Inclination angles during cross-slope roof walking.

Residential roofers have the highest rate of falls in the construction sector with injuries and fatalities costing billions of dollars annually. The s...

A systematic literature review of automatic Alzheimer's disease detection from speech and language.

OBJECTIVE: In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing....

Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection.

Normalizing intrinsic variabilities (e.g., variability in speech production brought on by aging, physical or cognitive task stress, Lombard effect, et...

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjec...

Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.

Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatmen...

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging p...

Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive func...

[A comparative study on treatment of lumbar degenerative disease with osteoporosis by manual and robot-assisted cortical bone trajectory screws fixation].

OBJECTIVE: To compare the safety and accuracy of manual and robot-assisted cortical bone trajectory (CBT) screws fixation in the treatment of lumbar d...

A new method to predict anomaly in brain network based on graph deep learning.

Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent y...

Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.

Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional ...

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

BACKGROUND: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to ide...

Can robots express facial emotions dominantly enough for use in dementia care?

Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known a...

Predicting Age with Deep Neural Networks from Polysomnograms.

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyz...

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular dis...

Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks.

Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and...

Detecting falls and estimation of daily habits with depth images using machine learning algorithms.

Different approaches have been proposed in the literature to detect the fall of an elderly person. In this paper, we propose a fall detection method b...