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Deep Learning-Based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges.

Dang Nguyen, Minh Huu Nhat Le, Trung Q Le

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary

    MDCare, a deep learning system, precisely quantifies surgical blood loss using sponges. This advanced technology offers real-time data, improving patient safety and surgical outcomes over traditional methods.

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    Area of Science:

    • Medical Technology
    • Artificial Intelligence in Surgery
    • Surgical Informatics

    Background:

    • Intraoperative blood loss quantification is vital for patient safety and surgical success.
    • Traditional visual estimation methods are subjective and inaccurate.
    • There is a need for precise, real-time blood loss monitoring systems.

    Purpose of the Study:

    • To introduce and validate MDCare, a deep learning-integrated system for accurate blood loss quantification using surgical sponges.
    • To demonstrate the superiority of MDCare over traditional estimation techniques.

    Main Methods:

    • Integration of mass sensor and webcam with deep learning algorithms (ResNet-18, YOLOv4).
    • Real-time image processing at 7.4 frames per second.
    • Validation using both synthetic and real blood scenarios.

    Main Results:

    • Achieved up to 96.2% classification accuracy for blood loss.
    • Demonstrated over 91% sponge detection accuracy.
    • System operates in real-time, suitable for surgical environments.

    Conclusions:

    • MDCare significantly enhances blood loss estimation accuracy compared to traditional methods.
    • The system supports surgeons with real-time data for improved decision-making.
    • MDCare represents a significant advancement in surgical care, with potential for widespread adoption.