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Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

Cardiology journal
IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) app...

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals.

Computer methods in biomechanics and biomedical engineering
Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful...

Machine learning assisted classification between diabetic polyneuropathy and healthy subjects using plantar pressure and temperature data: a feasibility study.

Computer methods in biomechanics and biomedical engineering
Automated and early detection of diabetics with polyneuropathy in an ambulatory health monitoring setup may reduce the major risk factors for diabetic patients. Increased and localized plantar pressure associated with impaired pain and temperature is...

Comparison Between an Expert Operator an Inexperienced Operator, and Artificial Intelligence Software: A Brief Clinical Study of Cephalometric Diagnostic.

The Journal of craniofacial surgery
INTRODUCTION: Artificial intelligence (AI) is constantly developing in several medical areas and has become useful to assist with treatment planning. Orthodontics and maxillofacial surgery use AI-based technology to identify and select cephalometric ...

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs.

New Model and Public Online Prediction Platform for Risk Stratification of Vocal Cord Leukoplakia.

The Laryngoscope
OBJECTIVE: To extract texture features from vocal cord leukoplakia (VCL) images and establish a VCL risk stratification prediction model using machine learning (ML) techniques.

Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson's disease patients with end-of-dose wearing-off.

Journal of neuroscience methods
OBJECTIVE: this study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson's disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classifi...

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

Journal of neuroscience methods
BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize an...

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

Diabetes, obesity & metabolism
AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.

Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study.

British journal of cancer
BACKGROUND: Neuroendocrine tumours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models c...