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

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 physio...

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Bibliographic Details
Published in:Bioengineering (Basel) 2024-01, Vol.11 (2), p.109
Main Authors: Avital, Guy, Hernandez Torres, Sofia I, Knowlton, Zechariah J, Bedolla, Carlos, Salinas, Jose, Snider, Eric J
Format: Article
Language:English
Subjects:
3-D printers
Abdomen
Actuation
Algorithms
Artificial intelligence
Automation
Biotechnology & Applied Microbiology
Color
Doppler effect
Engineering
hemodynamics
Hemorrhage
Image classification
Image processing
Innovations
machine learning
Medical equipment
Neural networks
Occlusion
Physiological research
Physiology
Pressure
tissue phantom
tourniquet application
Tourniquets
Ultrasonic imaging
Ultrasound
ultrasound imaging
Veins & arteries
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Summary: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.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11020109