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A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine
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Organ boundary determination algorithm for detecting internal bleeding.

Keiichiro Ito1, Shigeki Sugano, Hiroyasu Iwata

  • 1Department of Creative Science and Engineering, School of Modern Mechanical Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, Tokyo, 162-0044, Japan. itokei-1985@sugano.mech.waseda.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study introduces an automated system using ultrasound image processing to detect internal bleeding, improving upon the low sensitivity of traditional methods. The new approach achieved higher accuracy in identifying bleeding between the liver and kidney.

Area of Science:

  • Medical Imaging
  • Robotics
  • Ultrasound Technology

Background:

  • Internal bleeding is a critical condition often diagnosed using Focused Assessment with Sonography for Trauma (FAST).
  • The FAST protocol exhibits limited sensitivity (approximately 42.7%), potentially delaying crucial interventions for patients in shock.

Purpose of the Study:

  • To develop an automated robotic system for internal bleeding detection.
  • To enhance diagnostic sensitivity through advanced ultrasound (US) image processing techniques.
  • To improve the accuracy of organ boundary determination for more reliable internal bleeding identification.

Main Methods:

  • Development of algorithms for organ boundary determination using low-brightness set analysis.
  • Integration of these algorithms into an automated robotic system for internal bleeding detection.

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  • Utilizing clinical ultrasound images for system validation.

    • Main Results:

    • The proposed method successfully detected internal bleeding between the liver and kidney in clinical ultrasound images.
    • Achieved a sensitivity of 77.8% for internal bleeding detection.
    • Demonstrated a high specificity of 95.7%.

    Conclusions:

    • The developed automated system significantly improves upon the sensitivity of traditional FAST examinations.
    • The organ boundary determination method based on low-brightness set analysis is effective for detecting internal bleeding.
    • This robotic ultrasound system shows promise for more accurate and efficient diagnosis of internal bleeding.