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Heart Diseases

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Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT).

Current medical imaging
BACKGROUND: Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to ...

Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (fast chemical shift encoding [FastCSE]) to accelerate CSE.

AI-Supported Echocardiography for the Detection of Heart Diseases - A Scoping Review.

Studies in health technology and informatics
INTRODUCTION: Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the urgent need for accurate and efficient diagnostic tools. Echocardiography, a non-invasive imaging technique, plays a central role in the diagnosis of h...

Effective cardiac disease classification using FS-XGB and GWO approach.

Medical engineering & physics
Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment. Machine learning (ML)-based algorithms that use features extracted from electrocardiogram (ECG) si...

A novel approach for heart disease prediction using hybridized AITHO algorithm and SANFIS classifier.

Network (Bristol, England)
In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendatio...

Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).

Scientific reports
Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to pre...

Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

Minerva cardiology and angiology
The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have rema...

A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection.

Computers in biology and medicine
Heart diseases remain one of the leading causes of death worldwide. As a result, early and accurate diagnostics have become an urgent need for treatment and management. Most of the conventional methods adopted tend to have major drawbacks: the issues...

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

Computers in biology and medicine
For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease ris...