AIMC Topic: Aged

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Automated Assessment of Oral Diadochokinesis in Multiple Sclerosis Using a Neural Network Approach: Effect of Different Syllable Repetition Paradigms.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Slow and irregular oral diadochokinesis represents an important manifestation of spastic and ataxic dysarthria in multiple sclerosis (MS). We aimed to develop a robust algorithm based on convolutional neural networks for the accurate detection of syl...

Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.

International journal of medical informatics
INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Theref...

Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously m...

Using path signatures to predict a diagnosis of Alzheimer's disease.

PloS one
The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input p...

Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: EGD is the most vital procedure for the diagnosis of upper GI lesions. We aimed to compare the performance of unsedated ultrathin transoral endoscopy (U-TOE), unsedated conventional EGD (C-EGD), and sedated C-EGD with or without ...

Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury.

Journal of neurotrauma
Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI) cohorts, few studies have examined modern ML algorithms. To develop a simple ML model for TBI outco...

From manual to artificial intelligence fitting: Two cochlear implant case studies.

Cochlear implants international
To assess whether CI programming by means of a software application using artificial intelligence (AI), FOX®, may improve cochlear implant (CI) performance. Two adult CI recipients who had mixed auditory results with their manual fitting were selec...

Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Radiology
Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VF...

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Circulation. Cardiovascular imaging
BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volume...