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Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques

人工智能结合磁共振成像和计算建模在心脏电生理与临床诊断中的应用

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Abstract

The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases, which are the most common cause of morbidity and mortality worldwide, have gotten a lot of attention and been widely explored in recent decades. Along the way, techniques such as medical imaging, computing modeling, and artificial intelligence (AI) have always played significant roles in above studies. In this article, we illustrated the applications of AI in cardiac electrophysiological research and disease prediction. We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques. The main challenges and perspectives were also analyzed.

摘要

心血管疾病是导致死亡的最常见的原因之一。一直以来, 其内在的电生理机制和临床治疗方法都广受关注, 而医学成像、计算建模和人工智能是其中非常重要的研究技术。本文概括了人工智能的基本原理, 并重点论述了人工智能结合磁共振成像和计算建模在心脏基础与临床研究中的应用, 并探讨了这些技术目前面临的挑战及发展前景。

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Authors and Affiliations

  1. College of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China

    Heqing Zhan  (詹何庆), Guilai Han  (韩贵来) & Chuan’an Wei  (魏传安)

  2. Radiology Department, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 570102, China

    Zhiqun Li  (李治群)

Authors
  1. Heqing Zhan  (詹何庆)
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  2. Guilai Han  (韩贵来)
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  3. Chuan’an Wei  (魏传安)
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  4. Zhiqun Li  (李治群)
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Corresponding author

Correspondence to Zhiqun Li  (李治群).

Additional information

Foundation item: the Hainan Provincial Natural Science Foundation of China (No. 820RC625), and the National Natural Science Foundation of China (No. 82060332)

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Zhan, H., Han, G., Wei, C. et al. Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques. J. Shanghai Jiaotong Univ. (Sci.) (2023). https://doi.org/10.1007/s12204-023-2628-5

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  • DOI: https://doi.org/10.1007/s12204-023-2628-5

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