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Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...

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Related Experiment Video

Updated: Jul 14, 2026

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography
11:12

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

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Developing a Multimodal Prototype Device for Tissue Perfusion Monitoring.

Ivan Soto, Brendan Tran Morris, Barry Claman

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a machine learning system using thermal IR and RGB imaging to monitor tissue perfusion after surgery. It effectively detects arterial and venous blockages, aiming to reduce reliance on over-extended nursing staff.

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    Area of Science:

    • Biomedical Engineering
    • Medical Imaging
    • Machine Learning

    Background:

    • Post-vascular anastomosis repair requires 48-72 hours of close tissue monitoring.
    • Current monitoring relies heavily on nursing staff, leading to over-extension.

    Purpose of the Study:

    • To develop a multimodal tissue perfusion monitoring system using machine learning.
    • To enable unattended alerts for post-operative complications.

    Main Methods:

    • Utilized thermal IR, RGB, and laser speckle imaging modalities.
    • Employed a ResNet backbone with early and late fusion for data integration.
    • Compared the utility of different image modalities for classifying arterial and venous blockage.

    Main Results:

    • Late fusion of thermal IR and RGB images demonstrated superior performance in classification.
    • The system showed potential for unattended monitoring of tissue perfusion.

    Conclusions:

    • Multimodal imaging combined with machine learning offers a promising solution for post-operative tissue monitoring.
    • The developed system can potentially improve patient safety and reduce healthcare professional workload.