Real-Time Deployment of Ultrasound Image Interpretation AI Models for Emergency Medicine Triage Using a Swine Model - Sofia I. Hernandez Torres; Lawrence Holland; Theodore Winter; Ryan Ortiz; Krysta-Lynn Amezcua; Austin Ruiz; Catherine R. Thorpe; Eric J. Snider (Technologies)
Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury.