Publications

Assessment of trauma-induced hemorrhage with ultrasound is particularly challenging outside of the clinic, where its detection is crucial. The current clinical standard for hematoma detection – the focused assessment with sonography of trauma (FAST) exam – does not aim to detect ongoing blood loss, and thus is unable to detect injuries of increasing severity. To enhance detection of active bleeding, we propose the use of ultrasound contrast agents (UCAs), together with a novel flow phantom and contrast-sensitive processing techniques, to facilitate efficient, practical characterization of internal bleeding. Within a the custom phantom, UCAs and processing techniques enabled a significant enhancement of the hemorrhage visualization (mean increase in generalized contrast-to-noise ratio of 17 %) compared to the contrast-free case over a range of flow rates up to 40 ml/min. Moreover, we have shown that the use of UCAs improves the probability of detection: the area under the receiver operating characteristic curve for a flow rate of 40 ml/min was 0.99, compared to 0.72 without contrast. We also demonstrate how additional processing of the spatial and temporal information further localizes the bleeding site. UCAs also enhanced Doppler signals over the non-contrast case. These results show that specialized nonlinear processing (NLP) pipelines together with UCAs may offer an efficient means to improve substantially the detection of slower hemorrhages and increase survival rates for trauma-induced injury in pre-hospital settings.

Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.