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Heart Diseases

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Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently a...

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Sensors (Basel, Switzerland)
Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recogniti...

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.

Current cardiology reports
PURPOSE OF REVIEW: Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovas...

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Scientific data
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality...

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

Physical and engineering sciences in medicine
The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explor...

Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

BMC pediatrics
INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed natio...

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms.

International journal of cardiology
Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart dis...

Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.

Journal of medical Internet research
BACKGROUND: The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequit...

A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography.

Ultrasound in medicine & biology
OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essent...

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

Cardiovascular diabetology
BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical prac...