AIMC Topic: Heart Diseases

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Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics ...

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting.

Computational intelligence and neuroscience
The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of...

Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing.

Journal of echocardiography
Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report generation. In the field of echocardiography, AI offers the potential to enhance accuracy, speed up repor...

Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare.

Computer methods in biomechanics and biomedical engineering
In recent years, medical technological innovators have focused on diverse clinical therapies to find innovative ways to overcome clinical challenges. But still, there emerge certain drawbacks like high computational cost, increased error, less traini...

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.

Scientific reports
Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease i...

Construction of a new smooth support vector machine model and its application in heart disease diagnosis.

PloS one
Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to fin...

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Computers in biology and medicine
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess t...

Deep learning-based diagnosis of feline hypertrophic cardiomyopathy.

PloS one
Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods ...

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Computational intelligence and neuroscience
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning arc...

Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis.

Scientific reports
Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accel...