Publications

Internal bleeding due to non-compressible torso hemorrhage is the leading cause of prehospital fatalities in civilian and military trauma. A limited number of trauma surgeons are expected to be available in disaster scenarios and future large-scale combat operations. As a result, non-specialists will need to perform life-saving interventions to address internal bleeding. A first step in mitigation is ultrasound-guided central vascular access, which involves identifying a deep blood vessel in the imagery, such as the femoral vein, femoral artery, or internal jugular vein, and then placing a needle and catheter into the vessel for follow-on resuscitation. In this paper, we demonstrate machine learning algorithms for both femoral and neck vessel detection with high accuracy and real-time speed on smartphones. The algorithms are integrated with commercial ultrasound and optimized for use on low size, weight, and power devices. Coupled with custom robotics, this technology can enable rapid vascular access by non-specialist operators using a handheld platform.