Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

Journal Article · 24 January 2024 · Bioengineering

DOI: https://doi.org/10.3390/bioengineering11020109 · OSTI ID:2471221

Avital, Guy  ^[1]; Hernandez Torres, Sofia I.  ^[2]; Knowlton, Zechariah J.  ^[2]; Bedolla, Carlos ^[2]; Salinas, Jose ^[2]; Snider, Eric J.  ^[2]

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1. U.S. Army Institute of Surgical Research, San Antonio, TX (United States); Israel Defense Forces Medical Corps, Ramat Gan (Israel); Tel Aviv University (Israel)
2. U.S. Army Institute of Surgical Research, San Antonio, TX (United States)

Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

• Research Organization: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC); U.S. Army Medical Research and Development Command
• Grant/Contract Number: SC0014664
• OSTI ID: 2471221
• Journal Information: Bioengineering, Journal Name: Bioengineering Journal Issue: 2 Vol. 11; ISSN 2306-5354
• Publisher: MDPICopyright Statement
• Country of Publication: United States
• Language: English