Key result
Integrating numerical methods with machine learning enhances LV action potential simulations for elucidating disease mechanisms.
Why the study?
Deviations in the left ventricular action potential can cause arrhythmias, heart failure, and sudden cardiac death, making accurate understanding and modeling essential for elucidating disease mechanisms and developing treatments.
Answers are grounded in this paper and cite their sources.
This review outlines how integrating machine learning with traditional numerical methods can improve the modeling of left ventricular action potentials, aiding in the understanding of arrhythmias and heart failure.
The action potential is a pivotal electrical phenomenon that governs the contraction of cardiac muscle cells, known as cardiomyocytes. This rapid shift in membrane potential is orchestrated by the precise movement of ions across the cell membrane through specific ion channels. In the heart, action potentials are indispensable for initiating and coordinating the contractions of cardiac chambers, ensuring effective blood circulation throughout the body. The left ventricle, responsible for propelling oxygenated blood into the aorta and systemic circulation, is particularly critical. Deviations in the left ventricular action potential can result in severe cardiac conditions, including arrhythmias, heart failure, and sudden cardiac death. Consequently, an accurate understanding and modeling of the left ventricular action potential are essential for elucidating the mechanisms underlying these diseases and developing effective treatments. This paper provides a comprehensive overview of methodologies used to simulate the action potential within the left ventricle. It examines the historical evolution of electrophysiological models, foundational theories of cardiac action potentials, and their specific application to the left ventricle. The review encompasses the clinical relevance of these models and an analysis of simulation outcomes. This paper also explores the application of machine learning (ML) to enhance the accuracy and efficiency of action potential calculations within the left ventricle, highlighting various ML techniques and their potential impact on cardiology.
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Miličević et al. (2024) conducted a review in Left ventricular action potential simulations. Machine learning and numerical methods was evaluated. Integrating numerical methods with machine learning enhances the accuracy and efficiency of left ventricular action potential simulations for elucidating disease mechanisms.
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