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Purpose of Review <p>This review summarizes current applications of artificial intelligence (AI) in multimodality cardiac imaging for the evaluation of valvular heart disease (VHD).</p> Recent Findings <p>The prevalence of VHD continues to rise, placing increasing demands on cardiovascular imaging and longitudinal management. AI systems have been applied across echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) to automate image classification, segmentation, disease detection, and severity assessment. The most mature AI models have centered on transthoracic echocardiography (TTE), where deep learning (DL) frameworks enable whole-study interpretation and preliminary report generation. Applications in CCT and CMR remain in earlier stages but show promise for segmentation, tissue characterization, and pre-procedural planning.</p> Summary <p>AI has the potential to enhance the accuracy, reproducibility, and efficiency of imaging-based VHD assessment. Key challenges remain around generalizability, transparency, and clinical integration. Multidisciplinary collaboration is essential to ensure that AI complements, rather than replaces, human expertise.</p>

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The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月25日

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  • Cory Sejo,
  • Michael Randazzo,
  • Roberto Lang,
  • Jeremy Slivnick

摘要

Purpose of Review

This review summarizes current applications of artificial intelligence (AI) in multimodality cardiac imaging for the evaluation of valvular heart disease (VHD).

Recent Findings

The prevalence of VHD continues to rise, placing increasing demands on cardiovascular imaging and longitudinal management. AI systems have been applied across echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) to automate image classification, segmentation, disease detection, and severity assessment. The most mature AI models have centered on transthoracic echocardiography (TTE), where deep learning (DL) frameworks enable whole-study interpretation and preliminary report generation. Applications in CCT and CMR remain in earlier stages but show promise for segmentation, tissue characterization, and pre-procedural planning.

Summary

AI has the potential to enhance the accuracy, reproducibility, and efficiency of imaging-based VHD assessment. Key challenges remain around generalizability, transparency, and clinical integration. Multidisciplinary collaboration is essential to ensure that AI complements, rather than replaces, human expertise.