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The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification.

Jorge Oliveira, Francesco Renna, Paulo Dias Costa

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    This study introduces the largest pediatric heart sound dataset, featuring detailed murmur annotations. This resource aims to advance AI-driven diagnostic tools for heart conditions in children.

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    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Artificial Intelligence in Healthcare

    Background:

    • Cardiac auscultation is a cost-effective diagnostic method for heart conditions.
    • Current computer-assisted systems are limited by binary (normal/abnormal) phonocardiogram data.
    • Lack of large, detailed datasets hinders clinical application of auscultation-based AI.

    Purpose of the Study:

    • To create the largest publicly available pediatric heart sound dataset.
    • To enable advanced machine learning for cardiac murmur analysis.
    • To support the development of improved diagnostic systems for pediatric heart conditions.

    Main Methods:

    • Collected 5282 recordings from 1568 pediatric patients across four auscultation locations.
    • Manually annotated 215,780 heart sounds.
    • Provided expert annotations for cardiac murmurs, including timing, shape, pitch, grading, quality, and location.

    Main Results:

    • Established the largest pediatric heart sound dataset to date.
    • Detailed annotation of murmurs and their locations is now available.
    • The dataset includes comprehensive information on murmur characteristics.

    Conclusions:

    • The dataset facilitates research into advanced AI for cardiac diagnostics.
    • Detailed murmur annotations enable more sophisticated analysis beyond binary classification.
    • This resource is expected to accelerate the development of real-world clinical applications for heart sound analysis.