A Real-Time, Non-Invasive Monitoring System of Combat Casualties for Early Detection of Hemorrhagic Shock During Transport and Higher Echelon Medical

On the battlefield, medics must quickly determine injury severity, treat the greatest threats to life, diagnose hemorrhage and establish a triage order. The objective of this research project is to apply our active, long-term learning technology to the task of modeling and prediction of central blood volume parameters from extremely large, and variable physiological datasets. In Phase I, we applied our feature extraction and modeling techniques to data gathered from human lower body negative pressure studies simulating acute blood loss at the U.S. Army Institute of Surgical Research. We discovered several previously hidden hemodynamic relationships that are predictive of acute blood loss volume and an individual"s risk for cardiovascular collapse. This work has led to the development of an entirely new type of intelligent, non-invasive medical device, which we call CipherSensor. In Phase II we will integrate the CipherSensor technology into the FDA approved Nexfin monitor and Flashback"s own portable device based on pulse oximetry (CipherOx). These prototype devices will be used to collect acute blood loss data in clinical and experimental settings. The collected data will be used to build more robust and accurate models of early hemorrhage detection and resuscitation effectiveness, as well as preparation for pre-clinical tests.