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Related Experiment Video

Updated: Jan 9, 2026

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening
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Clinical Validation and Prospective Deployment of an Automated Deep Learning-Based Coronary Segmentation and Cardiac

Christian V Guthier, Christopher E Kehayias, Cosmin Ciausu

    Arxiv
    |December 3, 2025
    PubMed
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    An AI algorithm accurately segments heart structures for cardiac dose monitoring in cancer patients. This system enables real-time surveillance, identifies high-risk individuals, and supports coronary-sparing radiotherapy.

    Area of Science:

    • Radiotherapy and medical imaging
    • Artificial intelligence in oncology
    • Cardiovascular health in cancer patients

    Background:

    • Accurate segmentation of cardiac substructures is crucial for dose monitoring in radiotherapy.
    • Current methods for cardiac dose assessment can be time-consuming and lack real-time surveillance capabilities.
    • AI offers potential for automated and efficient analysis of cardiac structures and dose exposure.

    Purpose of the Study:

    • To develop and validate an AI algorithm for accurate segmentation of cardiac substructures.
    • To implement real-time surveillance of cardiac dose exposure in lung cancer patients.
    • To assess the association of AI-derived cardiac dose metrics with clinical outcomes.

    Main Methods:

    • A 3D UNet model was trained and validated on thoracic CT scans for auto-segmentation of cardiac substructures.

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  • The AI system was implemented for retrospective analysis of 3,399 lung cancer patients and prospective surveillance of 1,386 patients.
  • Geometric accuracy, dose-volume parameter concordance, and association with outcomes were evaluated.

    • Main Results:

    • The AI system demonstrated high accuracy in segmenting the heart and left anterior descending artery (LAD).
    • AI-derived LAD V15 was independently associated with major adverse cardiac events (MACE) and all-cause mortality (ACM).
    • Retrospective analysis showed a decline in LAD V15 over time, with 32% of patients identified as high-risk; prospective surveillance flagged 19% for potential referral.

    Conclusions:

    • A validated AI system accurately segments cardiac substructures and enables real-time dose surveillance.
    • Automated cardiac dose monitoring can facilitate the adoption of coronary-sparing radiotherapy.
    • The AI system provides point-of-care alerts for high-risk patients, improving follow-up care.