AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biosensors biosensors	4.9	6.6	2011	17.1 Days	CHF 2700
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600

15 pages, 1550 KiB

Open AccessArticle

Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation

by Jay F. Gupta, Saaid H. Arshad, Brian A. Telfer, Eric J. Snider and Victor A. Convertino

Biosensors 2022, 12(12), 1168; https://doi.org/10.3390/bios12121168 - 14 Dec 2022

Cited by 6 | Viewed by 2443

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. [...] Read more.

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. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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33 pages, 2582 KiB

Open AccessReview

Progress and Challenges of Point-of-Need Photonic Biosensors for the Diagnosis of COVID-19 Infections and Immunity

by Juanjuan Liu and Sebastian Wachsmann-Hogiu

Biosensors 2022, 12(9), 678; https://doi.org/10.3390/bios12090678 - 24 Aug 2022

Cited by 4 | Viewed by 2725

Abstract

The new coronavirus disease, COVID-19, caused by SARS-CoV-2, continues to affect the world and after more than two years of the pandemic, approximately half a billion people are reported to have been infected. Due to its high contagiousness, our life has changed dramatically, [...] Read more.

The new coronavirus disease, COVID-19, caused by SARS-CoV-2, continues to affect the world and after more than two years of the pandemic, approximately half a billion people are reported to have been infected. Due to its high contagiousness, our life has changed dramatically, with consequences that remain to be seen. To prevent the transmission of the virus, it is crucial to diagnose COVID-19 accurately, such that the infected cases can be rapidly identified and managed. Currently, the gold standard of testing is polymerase chain reaction (PCR), which provides the highest accuracy. However, the reliance on centralized rapid testing modalities throughout the COVID-19 pandemic has made access to timely diagnosis inconsistent and inefficient. Recent advancements in photonic biosensors with respect to cost-effectiveness, analytical performance, and portability have shown the potential for such platforms to enable the delivery of preventative and diagnostic care beyond clinics and into point-of-need (PON) settings. Herein, we review photonic technologies that have become commercially relevant throughout the COVID-19 pandemic, as well as emerging research in the field of photonic biosensors, shedding light on prospective technologies for responding to future health outbreaks. Therefore, in this article, we provide a review of recent progress and challenges of photonic biosensors that are developed for the testing of COVID-19, consisting of their working fundamentals and implementation for COVID-19 testing in practice with emphasis on the challenges that are faced in different development stages towards commercialization. In addition, we also present the characteristics of a biosensor both from technical and clinical perspectives. We present an estimate of the impact of testing on disease burden (in terms of Disability-Adjusted Life Years (DALYs), Quality Adjusted Life Years (QALYs), and Quality-Adjusted Life Days (QALDs)) and how improvements in cost can lower the economic impact and lead to reduced or averted DALYs. While COVID19 is the main focus of these technologies, similar concepts and approaches can be used and developed for future outbreaks of other infectious diseases. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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13 pages, 1326 KiB

Open AccessArticle

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

by Victor A. Convertino, Robert W. Techentin, Ruth J. Poole, Ashley C. Dacy, Ashli N. Carlson, Sylvain Cardin, Clifton R. Haider, David R. Holmes III, Chad C. Wiggins, Michael J. Joyner, Timothy B. Curry and Omer T. Inan

Sensors 2022, 22(7), 2642; https://doi.org/10.3390/s22072642 - 30 Mar 2022

Cited by 7 | Viewed by 2877

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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12 pages, 951 KiB

Open AccessHypothesis

Portable Medical Suction and Aspirator Devices: Are the Design and Performance Standards Relevant?

by Saketh R. Peri, Forhad Akhter, Robert A. De Lorenzo and R. Lyle Hood

Sensors 2022, 22(7), 2515; https://doi.org/10.3390/s22072515 - 25 Mar 2022

Cited by 2 | Viewed by 8420

Abstract

Airway clearance refers to the clearing of any airway blockage caused due to foreign objects such as mud, gravel, and biomaterials such as blood, vomit, or teeth fragments using the technology of choice, portable suction devices. Currently available devices are either too heavy [...] Read more.

Airway clearance refers to the clearing of any airway blockage caused due to foreign objects such as mud, gravel, and biomaterials such as blood, vomit, or teeth fragments using the technology of choice, portable suction devices. Currently available devices are either too heavy and bulky to be carried, or insufficiently powered to be useful despite being in accordance with the ISO 10079-1 standards. When applied to portable suction, the design and testing standards lack clinical relevancy, which is evidenced by how available portable suction devices are sparingly used in pre-hospital situations. Lack of clinical relevancy despite being in accordance with design/manufacturing standards arise due to little if any collaboration between those developing clinical standards and the bodies that maintain design and manufacturing standards. An updated set of standards is required that accurately reflects evidence-based requirements and specifications, which should promote valid, rational, and relevant engineering designs and manufacturing standards in consideration of the unique scenarios facing prehospital casualty care. This paper aims to critically review the existing standards for portable suction devices and propose modifications based on the evidence and requirements, especially for civilian prehospital and combat casualty care situations. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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13 pages, 2359 KiB

Open AccessArticle

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

by Jiawei Liang, Wei Zhang, Yu Qin, Ying Li, Gang Logan Liu and Wenjun Hu

Biosensors 2022, 12(3), 173; https://doi.org/10.3390/bios12030173 - 13 Mar 2022

Cited by 13 | Viewed by 4149

Abstract

The sudden outbreak of COVID-19 rapidly developed into a global pandemic, which caused tens of millions of infections and millions of deaths. Although SARS-CoV-2 is known to cause COVID-19, effective approaches to detect SARS-CoV-2 using a convenient, rapid, accurate, and low-cost method are [...] Read more.

The sudden outbreak of COVID-19 rapidly developed into a global pandemic, which caused tens of millions of infections and millions of deaths. Although SARS-CoV-2 is known to cause COVID-19, effective approaches to detect SARS-CoV-2 using a convenient, rapid, accurate, and low-cost method are lacking. To date, most of the diagnostic methods for patients with early infections are limited to the detection of viral nucleic acids via polymerase chain reaction (PCR), or antigens, using an enzyme-linked immunosorbent assay or a chemiluminescence immunoassay. This study developed a novel method that uses localized surface plasmon resonance (LSPR) sensors, optical imaging, and artificial intelligence methods to directly detect the SARS-CoV-2 virus particles without any sample preparation. The virus concentration can be qualitatively and quantitatively detected in the range of 125.28 to 10⁶ vp/mL through a few steps within 12 min with a limit of detection (LOD) of 100 vp/mL. The accuracy of the SARS-CoV-2 positive or negative assessment was found to be greater than 97%, and this was demonstrated by establishing a regression machine learning model for the virus concentration prediction (R² > 0.95). Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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9 pages, 592 KiB

Open AccessPerspective

Intelligent Clinical Decision Support

by Michael R. Pinsky, Artur Dubrawski and Gilles Clermont

Sensors 2022, 22(4), 1408; https://doi.org/10.3390/s22041408 - 12 Feb 2022

Cited by 8 | Viewed by 3153

Abstract

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to [...] Read more.

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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15 pages, 1699 KiB

Open AccessArticle

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

by Yekanth Ram Chalumuri, Jacob P. Kimball, Azin Mousavi, Jonathan S. Zia, Christopher Rolfes, Jesse D. Parreira, Omer T. Inan and Jin-Oh Hahn

Sensors 2022, 22(4), 1336; https://doi.org/10.3390/s22041336 - 10 Feb 2022

Cited by 6 | Viewed by 3011

Abstract

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only [...] Read more.

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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13 pages, 18686 KiB

Open AccessArticle

Development and Characterization of a Self-Tightening Tourniquet System

by Saul J. Vega, Sofia I. Hernandez-Torres, David Berard, Emily N. Boice and Eric J. Snider

Sensors 2022, 22(3), 1122; https://doi.org/10.3390/s22031122 - 1 Feb 2022

Cited by 2 | Viewed by 3115

Abstract

Uncontrolled hemorrhage remains a leading cause of death in both emergency and military medicine. Tourniquets are essential to stopping hemorrhage in these scenarios, but they suffer from subjective, inconsistent application. Here, we demonstrate how tourniquet application can be automated using sensors and computer [...] Read more.

Uncontrolled hemorrhage remains a leading cause of death in both emergency and military medicine. Tourniquets are essential to stopping hemorrhage in these scenarios, but they suffer from subjective, inconsistent application. Here, we demonstrate how tourniquet application can be automated using sensors and computer algorithms. The auto-tourniquet self-tightens until blood pressure oscillations are no longer registered by the pressure sensor connected to the pneumatic pressure cuff. The auto-tourniquet’s performance in stopping the bleed was comparable to manual tourniquet application, but the time required to fully occlude the bleed was longer. Application of the tourniquet was significantly smoother, and less variable, for the automatic tourniquet compared to manual tourniquet application. This proof-of-concept study highlights how automated tourniquets can be integrated with sensors to provide a much more consistent application and use compared to manual application, even in controlled, low stress testing conditions. Future work will investigate different sensors and tourniquets to improve the application time and repeatability. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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28 pages, 5971 KiB

Open AccessArticle

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

by Dong Han, Syed Khairul Bashar, Jesús Lázaro, Fahimeh Mohagheghian, Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily L. Dickson, Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy P. Fitzgibbons, David D. McManus and Ki H. Chon

Biosensors 2022, 12(2), 82; https://doi.org/10.3390/bios12020082 - 29 Jan 2022

Cited by 37 | Viewed by 9465

Abstract

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and [...] Read more.

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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12 pages, 803 KiB

Open AccessArticle

Automated Assessment of Cardiovascular Sufficiency Using Non-Invasive Physiological Data

by Xinyu Li, Michael R. Pinsky and Artur Dubrawski

Sensors 2022, 22(3), 1024; https://doi.org/10.3390/s22031024 - 28 Jan 2022

Cited by 4 | Viewed by 2222

Abstract

For fluid resuscitation of critically ill individuals to be effective, it must be well calibrated in terms of timing and dosages of treatments. In current practice, the cardiovascular sufficiency of patients during fluid resuscitation is determined using primarily invasively measured vital signs, including [...] Read more.

For fluid resuscitation of critically ill individuals to be effective, it must be well calibrated in terms of timing and dosages of treatments. In current practice, the cardiovascular sufficiency of patients during fluid resuscitation is determined using primarily invasively measured vital signs, including Arterial Pressure and Mixed Venous Oxygen Saturation (SvO2), which may not be available in outside-of-hospital settings, particularly in the field when treating subjects injured in traffic accidents or wounded in combat where only non-invasive monitoring is available to drive care. In this paper, we propose (1) a Machine Learning (ML) approach to estimate the sufficiency utilizing features extracted from non-invasive vital signs and (2) a novel framework to address the detrimental impact of inter-patient diversity on the ability of ML models to generalize well to unseen subjects. Through comprehensive evaluation on the physiological data collected in laboratory animal experiments, we demonstrate that the proposed approaches can achieve competitive performance on new patients using only non-invasive measurements. These characteristics enable effective monitoring of fluid resuscitation in real-world acute settings with limited monitoring resources and can help facilitate broader adoption of ML in this important subfield of healthcare. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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18 pages, 3838 KiB

Open AccessArticle

Supervisory Algorithm for Autonomous Hemodynamic Management Systems

by Eric J. Snider, Saul J. Vega, Evan Ross, David Berard, Sofia I. Hernandez-Torres, Jose Salinas and Emily N. Boice

Sensors 2022, 22(2), 529; https://doi.org/10.3390/s22020529 - 11 Jan 2022

Cited by 7 | Viewed by 1926

Abstract

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these [...] Read more.

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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20 pages, 3624 KiB

Open AccessArticle

AI-Enabled, Ultrasound-Guided Handheld Robotic Device for Femoral Vascular Access

by Laura J. Brattain, Theodore T. Pierce, Lars A. Gjesteby, Matthew R. Johnson, Nancy D. DeLosa, Joshua S. Werblin, Jay F. Gupta, Arinc Ozturk, Xiaohong Wang, Qian Li, Brian A. Telfer and Anthony E. Samir

Biosensors 2021, 11(12), 522; https://doi.org/10.3390/bios11120522 - 18 Dec 2021

Cited by 27 | Viewed by 13721

Abstract

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well [...] Read more.

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique’s robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 ± 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 ± 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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19 pages, 4252 KiB

Open AccessArticle

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum

by Michael Chan, Venu G. Ganti, J. Alex Heller, Calvin A. Abdallah, Mozziyar Etemadi and Omer T. Inan

Biosensors 2021, 11(12), 521; https://doi.org/10.3390/bios11120521 - 17 Dec 2021

Cited by 19 | Viewed by 6453

Abstract

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO₂) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO₂, we attempted [...] Read more.

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO₂) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO₂, we attempted to demonstrate accurate SpO₂ estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO₂ from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO₂ was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPG_green-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO₂. The accurate SpO₂ estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications. Full article

(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)

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