About this Research Topic
This Research Topic, titled “Artificial Intelligence for Arrhythmia Detection and Prediction”, is dedicated to exploring the transformative potential of AI and Deep Learning (DL) in accurate arrhythmia detection and prediction. We extend an invitation to authors to contribute their expertise, aiming to propel research towards the ultimate objective of detecting arrhythmias accurately using data from different devices. Through the collective efforts of contributors, we aim to foster a deeper understanding of the role of AI in reshaping the paradigm of arrhythmia care, ultimately enhancing patient outcomes and advancing the field of cardiovascular medicine.
We invite authors to contribute to this Research Topic by submitting manuscripts that delve into the application of AI for arrhythmia detection and prediction of their future onset. Topics of interest include, but are not limited to: advanced DL / AI methodologies and innovative approaches for analyzing data from diverse modalities, such as 12-lead ECG machines, Holter or inpatient ECG monitoring devices and wearables, for this cause.
Authors are encouraged to elucidate the potential of their contributions in realizing the ultimate goal—detecting arrhythmias before clinical manifestation.
We welcome Original Research, Brief Research Report, Clinical Trials, Study Protocols, Systematic Reviews, and Technology and Code articles that contribute to the advancement of knowledge in this transformative area of cardiovascular medicine. Together, let us pave the way for a future where AI plays a pivotal role in predicting and preventing arrhythmias, ultimately enhancing patient outcomes and reshaping the landscape of cardiovascular care.
Keywords: arrhythmias, artificial intelligence, deep learning, precision medicine
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.