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Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

Heart rhythm
BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often ...

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

Journal of imaging informatics in medicine
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1...

Effects of track-based stair climbing robot on muscle activity, usability, and psychological anxiety: a preliminary study.

Disability and rehabilitation. Assistive technology
This study investigated the effects of using the LiftCar-150 track-based stair-climbing robot on muscle activity, usability, and psychological anxiety. While stair-climbing robots enhance mobility for individuals with physical disabilities, existing ...

Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.

NMR in biomedicine
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled p...

A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery.

American journal of surgery
BACKGROUND: Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data...

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

Computer methods and programs in biomedicine
INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

Journal of neuroscience methods
BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imagi...

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Artificial intelligence in medicine
Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical s...

A preliminary study on the effects of long-term robot suit exercise training on gait function and quality of life in patients with spinal and bulbar muscular atrophy.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Spinal and bulbar muscular atrophy (SBMA) progressively impairs gait function, resulting in the need for patients to use a wheelchair approximately 20 years after onset. No reports have investigated the effects of long-term exercise training using th...