AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

Design and evaluation of an imager for assessing wound inflammatory responses and bioburden in a pig model.

Our work details the development and characterization of a portable luminescence imaging device for detecting inflammatory responses and infection in skin wounds. The device includes a CCD camera and close-up lens integrated into a customizable 3D printed imaging chamber to create a portable light-tight imager for luminescence imaging. The chamber has an adjustable light portal that permits ample ambient light for white light imaging. This imager was used to quantify in real time the extent of two-dimensional reactive oxygen species (ROS) activity distribution using a porcine wound infection model. The imager was used to successfully visualize ROS-associated luminescent activities in vitro and in vivo. Using a pig full-thickness cutaneous wound model, we further demonstrate that this portable imager can detect the change of ROS activities and their relationship with vasculature in the wound environment. Finally, by analyzing ROS intensity and distribution, an imaging method was developed to distinguish infected from uninfected wounds. We discovered a distinct ROS pattern between bacteria-infected and control wounds corresponding to the microvasculature. The results presented demonstrate that this portable luminescence imager is capable of imaging ROS activities in cutaneous wounds in a large animal model, indicating suitability for future clinical applications.

Design of a portable imager for near-infrared visualization of cutaneous wounds.

A portable imager developed for real-time imaging of cutaneous wounds in research settings is described. The imager consists of a high-resolution near-infrared CCD camera capable of detecting both bioluminescence and fluorescence illuminated by an LED ring with a rotatable filter wheel. All external components are integrated into a compact camera attachment. The device is demonstrated to have competitive performance with a commercial animal imaging enclosure box setup in beam uniformity and sensitivity. Specifically, the device was used to visualize the bioluminescence associated with increased reactive oxygen species activity during the wound healing process in a cutaneous wound inflammation model. In addition, this device was employed to observe the fluorescence associated with the activity of matrix metalloproteinases in a mouse lipopolysaccharide-induced infection model. Our results support the use of the portable imager design as a noninvasive and real-time imaging tool to assess the extent of wound inflammation and infection.