Timing of the intervention for intracranial hematomas is critical for its success, specifically since expansion of the hemorrhage can result in debilitating and sometimes fatal outcomes. Led by Britton Chance, we and an extended team from University of Pennsylvania, Baylor and Drexel universities developed a handheld brain hematoma detector for early triage and diagnosis of head trauma victims. After obtaining de novo Food and Drug Administration clearance, over 200 systems are deployed in all Marine battalion aid stations around the world. Infrascanner, a handheld brain hematoma detection system, is based on the differential near-infrared light absorption of the injured versus the noninjured part of brain. About 12 independent studies have been conducted in the USA, Canada, Spain, Italy, the Netherlands, Germany, Russia, Poland, Afghanistan, India, China, and Turkey. Here, we outline the background and design of the device as well as clinical studies with a total of 1293 patients and 203 hematomas. Infrascanner demonstrates high sensitivity (adults: 92.5% and children: 93%) and specificity (adults: 82.9% and children: 86.5%) in detecting intracranial hematomas >3.5 mL in volume and <2.5 cm from the surface of the brain. Infrascanner is a clinically effective screening solution for head trauma patients in prehospital settings where timely triage is critical.
Anesthesia monitoring currently needs a reliable method to evaluate the effects of the anesthetics on its primary target, the brain. This study focuses on investigating the clinical usability of a functional near-infrared spectroscopy (fNIRS)-derived machine learning classifier to perform automated and real-time classification of maintenance and emergence states during sevoflurane anesthesia. For 19 surgical procedures, we examine the entire continuum of the maintenance–transition–emergence phases and evaluate the predictive capability of a support vector machine (SVM) classifier during these phases. We demonstrate the robustness of the predictions made by the SVM classifier and compare its performance with that of minimum alveolar concentration (MAC) and bispectral (BIS) index-based predictions. The fNIRS-SVM investigated in this study provides evidence to the usability of the fNIRS signal for anesthesia monitoring. The method presented enables classification of the signal as maintenance or emergence automatically as well as in real-time with high accuracy, sensitivity, and specificity. The features local mean HbTotal, std HbO2, local min Hb and HbO2, and range Hb and HbO2 were found to be robust biomarkers of this binary classification task. Furthermore, fNIRS-SVM was capable of identifying emergence before movement in a larger number of patients than BIS and MAC.
Near infrared spectroscopy as a neuroimaging modality is a recent development. Near infrared neuroimagers are typically safe, portable, relatively affordable and non-invasive. The ease of sensor setup and non-intrusiveness make functional near infrared (fNIR) imaging an ideal candidate for monitoring human cortical function in a wide range of real world situations. However optical signals are susceptible to motion-artifacts, hindering the application of fNIR in studies where subject mobility cannot be controlled. In this paper, we present a filtering framework for motion-artifact cancellation to facilitate the deployment of fNIR imaging in real-world scenarios. We simulate a generic field environment by having subjects walk on a treadmill while performing a cognitive task and demonstrate that measurements can be effectively cleaned of motion-artifacts.
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