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Integrating Radiomics with Machine Learning to Improve Detection of Premature Ventricular Contractions

  • By

  • Jingjie Liu

  • Shiyu Dai

  • Lingxuan Hou

  • Boyang Zang

  • Yang Liu

  • Chongfu Jia

  • Xiaomeng Yin

  • May 15, 2026

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  • 1

    Premature Ventricular Contractions (PVC) can lead to serious cardiac issues, including left ventricular enlargement and increased risk of lethal arrhythmias.

  • 2

    ECG is commonly used for PVC localization but struggles with differentiating between closely located origins like LVOT and RVOT.

  • 3

    CCTA-based radiomics can identify microstructural changes associated with PVC, offering insights beyond what standard ECG can provide.

  • 4

    A hierarchical machine learning framework was developed to integrate CCTA radiomics and clinical data for improved PVC localization.

  • 5

    The study involved 304 patients undergoing RFCA, focusing on developing a machine learning model for accurate PVC origin localization.

Original Source(s)

Combining radiomics and machine learning for enhanced localization of premature ventricular contractions

  • by Jingjie Liu, Shiyu Dai, Lingxuan Hou, Boyang Zang, Yang Liu, Chongfu Jia, Xiaomeng Yin

  • May 15, 2026

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