AIMC Topic: Aged, 80 and over
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Artificial neural network optimizes self-examination of osteoporosis risk in women.
The Journal of international medical research
Jun 10, 2019
OBJECTIVE: This study aimed to investigate the application of an artificial neural network (ANN) in optimizing the Osteoporosis Self-Assessment Tool for Asians (OSTA) score.
Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.
BioMed research international
Jun 9, 2019
OBJECTIVE: Deep vein thrombosis (DVT) is a disease caused by abnormal blood clots in deep veins. Accurate segmentation of DVT is important to facilitate the diagnosis and treatment. In the current study, we proposed a fully automatic method of DVT de...
On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm - Clinical Effectiveness in a Retrospective Multicenter Cohort.
Circulation journal : official journal of the Japanese Circulation Society
Jun 8, 2019
BACKGROUND: This study evaluated the diagnostic capability of on-site coronary computed tomography-derived computational fractional flow reserve (CT-FFR) determinations for detecting coronary artery disease (CAD), as assessed by invasive fractional f...
FriWalk robotic walker: usability, acceptance and UX evaluation after a pilot study in a real environment.
Disability and rehabilitation. Assistive technology
Jun 7, 2019
: Scientific evidence supports that prevention strategies like multicomponent physical exercise help avoiding functional decline, falls and frailty. The robotic walker FriWalk, developed within the ACANTO project, supports the execution of controlled...
Using heart rate profiles during sleep as a biomarker of depression.
BMC psychiatry
Jun 7, 2019
BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart ...
Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis.
IEEE transactions on cybernetics
Jun 5, 2019
Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each ta...
Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests.
The Journal of arthroplasty
Jun 5, 2019
BACKGROUND: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and ...
Effects of Transcranial Direct Current Stimulation (tDCS) Combined With Wrist Robot-Assisted Rehabilitation on Motor Recovery in Subacute Stroke Patients: A Randomized Controlled Trial.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 3, 2019
Both transcranial direct current stimulation (tDCS) and wrist robot-assisted training have demonstrated to be promising approaches for stroke rehabilitation. However, the effects of the combination of the two treatments in subacute stroke patients ar...
Biomechanical Phenomena
Male
Middle Aged
Adolescent
Treatment Outcome
Female
Aged
Humans
Aged, 80 and over
Algorithms
Young Adult
Adult
Combined Modality Therapy
Electrodes
Upper Extremity
Stroke Rehabilitation
Recovery of Function
Transcranial Direct Current Stimulation
Robotics
Motor Cortex
Single-Blind Method
Electroencephalography
Wrist
Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.
PloS one
Jun 3, 2019
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearin...
Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.
Ophthalmology
May 31, 2019
PURPOSE: To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images.
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