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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Space shuttle operations at the NASA Kennedy Space Center: the role of emergency medicine

The Division of Emergency Medicine at the University of Florida coordinates a unique program with the NASA John F. Kennedy Space Center (KSC) to provide emergency medical support (EMS) for the United States Space Transportation System. This report outlines the organization of the KSC EMS system, training received by physicians providing medical support, logistic and operational aspects of the mission, and experiences of team members. The participation of emergency physicians in support of manned space flight represents another way that emergency physicians provide leadership in prehospital care and disaster management.

Emergency Medicine/education/organization & admini↗
NASA NTRS1994

Human factors and safety in emergency medicine

A model based on an input process and outcome conceptualisation is suggested to address safety-relevant factors in emergency medicine. As shown in other dynamic and demanding environments, human factors play a decisive role in attaining high quality service. Attitudes held by health-care providers, organisational shells and work-cultural parameters determine communication, conflict resolution and workload distribution within and between teams. These factors should be taken into account to improve outcomes such as operational integrity, job satisfaction and morale.

Non-NASA Center↗
NASA NTRS1980

Emergency medicine and the airline passenger

Problems related to immediate medical care in case of in-flight emergencies are discussed with reference to such critical types of medical emergencies as obstructed airway, cardiac dysfunction, trauma, hemorrhage, hypoxia, and pain. It is shown that training flight attendants to deal with in-flight medical emergencies and to use first-aid support equipment and essential and useful drugs may later help with stabilization of a victim and allow continuing the flight to the scheduled destination without the need for a diverted landing. Among the steps suggested in order to upgrade inflight welfare and safety of passengers are the development of an advisory circular by the FAA covering standardized training for flight attendants, regulatory action requiring upgrading of the present rudimentary first-aid kit, and the enactment of Good Samaritan legislation by the U.S. Government.

Mohler, S. R.↗
NASA NTRS1991

International disaster research

No existing telecommunications system can be expected to provide strategy and tactics appropriate to the complex, many faceted problem of disaster. Despite the exciting capabilities of space, communications, remote sensing, and the miracles of modern medicine, complete turnkey transfers to the disaster problem do not make the fit, and cannot be expected to do so. In 1980, a Presidential team assigned the mission of exploring disaster response within the U.S. Federal Government encountered an unanticipated obstacle: disaster was essentially undefined. In the absence of a scientifically based paradigm of disaster, there can be no measure of cost effectiveness, optimum design of manpower structure, or precise application of any technology. These problems spawned a 10-year, multidisciplinary study designed to define the origins, anatomy, and necessary management techniques for catastrophes. The design of the study necessarily reflects interests and expertise in disaster medicine, emergency medicine, telecommunications, computer communications, and forencsic sciences. This study is described.

Silverstein, Martin Elliot↗
DOE OSTI2024

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Ventilator Technologies Sustain Critically Injured Patients

Consider this scenario: A soldier has been critically wounded in a sudden firefight in a remote region of Afghanistan. The soldier s comrades attend to him and radio for help, but the soldier needs immediate medical expertise and treatment that is currently miles away. The connection between medical support for soldiers on the battlefield and astronauts in space may not be immediately obvious. But when it comes to providing adequate critical care, NASA and the military have very similar operational challenges, says Shannon Melton of NASA contractor Wyle Integrated Science and Engineering. Melton works within Johnson Space Center s Space Medicine Division, which supports astronaut crew health before, during, and after flight. In space, we have a limited number of care providers, and those providers are not always clinicians with extensive medical training. We have limited room to provide care, limited consumables, and our environment is not like that of a hospital, she says. The Space Medicine Division s Advanced Projects Group works on combining the expertise of both clinicians and engineers to develop new capabilities that address the challenges of medical support in space, including providing care to distant patients. This field, called telemedicine, blends advanced communications practices and technologies with innovative medical devices and techniques to allow caregivers with limited or no medical experience to support a patient s needs. NASA, just by its nature, has been doing remote medicine since the beginning of the Space Program, says Melton, an engineer in the Advanced Projects Group. Since part of NASA s mandate is to transfer the results of its technological innovation for the benefit of the public, the Agency has worked with doctors and private industry to find ways to apply the benefits of space medicine on Earth. In one such case, a NASA partnership has resulted in new technologies that may improve the quality of emergency medicine for wounded soldiers on the battlefield and regular civilians.

Source record↗
NASA NTRS2014

Optic Nerve Sheath Diameter: Translating a Terrestrial Focused Technique Into a Clinical Monitoring Tool for Space Flight

Emergency medicine physicians recently adopted the use of ultrasonography to quickly measure optic nerve sheath diameter (ONSD) as concomitant with increased intracranial pressure. NASA Space and Clinical Operations Division has been using ground and on-orbit ultrasound capabilities since 2009 to consider this anatomical measure as a proxy for intracranial pressure in the microgravity environment. In the terrestrial emergency room population, an ONSD greater than 0.59 cm is considered highly predictive of elevated intracranial pressure. However, this cut-off limit is not applicable to the spaceflight setting since over 50% of US Operating Segment (USOS) astronauts have an ONSD greater than 0.60 cm even before missions. Crew Surgeon clinical decision-making is complicated by the fact that many astronauts have history of previous spaceflights. Data will be presented characterizing the distribution of baseline ONSD in the astronaut corps, longitudinal trends in-flight, and the predictive power of this measure related to increased intracranial pressure outcomes.

Mason, Sara S.↗
NASA NTRS2007

Clinical Core Competency Training for NASA Flight Surgeons

Introduction: The cohort of NASA flight surgeons (FS) is a very accomplished group with varied clinical backgrounds; however, the NASA Flight Surgeon Office has identified that the extremely demanding schedule of this cohort prevents many of these physicians from practicing clinical medicine on a regular basis. In an effort to improve clinical competency, the NASA FS Office has dedicated one day a week for the FS to receive clinical training. Each week, an FS is assigned to one of five clinical settings, one being medical patient simulation. The Medical Operations Support Team (MOST) was tasked to develop curricula using medical patient simulation that would meet the clinical and operational needs of the NASA FS Office. Methods: The MOST met with the Lead FS and Training Lead FS to identify those core competencies most important to the FS cohort. The MOST presented core competency standards from the American Colleges of Emergency Medicine and Internal Medicine as a basis for developing the training. Results: The MOST identified those clinical areas that could be best demonstrated and taught using medical patient simulation, in particular, using high fidelity human patient simulators. Curricula are currently being developed and additional classes will be implemented to instruct the FS cohort. The curricula will incorporate several environments for instruction, including lab-based and simulated microgravity-based environments. Discussion: The response from the NASA FS cohort to the initial introductory class has been positive. As a result of this effort, the MOST has identified three types of training to meet the clinical needs of the FS Office; clinical core competency training, individual clinical refresher training, and just-in-time training (specific for post-ISS Expedition landings). The MOST is continuing to work with the FS Office to augment the clinical training for the FS cohort, including the integration of Web-based learning.

Polk, J. D.↗
DOE OSTI2024

Entropy removal of medical diagnostics

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Design and testing of ultrasound probe adapters for a robotic imaging platform

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited. Instead, robotics and computer vision technology can simplify image acquisition. As a first step towards this larger goal, here, we focus on the development of prototypes for ultrasound probe securement using a robotics platform. The ability of four probe adapter technologies to precisely capture images at anatomical locations, repeatedly, and with different ultrasound transducer types were evaluated across more than five scoring criteria. Testing demonstrated two of the adapters outperformed the traditional robot gripper and manual image capture, with a compact, rotating design compatible with wireless imaging technology being most suitable for use at the point of injury. Next steps will integrate the robotic platform with computer vision and deep learning image interpretation models to automate image capture and diagnosis. This will lower the skill threshold needed for medical imaging-based triage, enabling this procedure to be available at or near the point of injury.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Development of a Multi-Channel, High Frequency QRS Electrocardiograph

With the advent of the ISS era and the potential requirement for increased cardiovascular monitoring of crewmembers during extended EVAs, NASA flight surgeons would stand to benefit from an evolving technology that allows for a more rapid diagnosis of myocardial ischemia compared to standard electrocardiography. Similarly, during the astronaut selection process, NASA flight surgeons and other physicians would also stand to benefit from a completely noninvasive technology that, either at rest or during maximal exercise tests, is more sensitive than standard ECG in identifying the presence of ischemia. Perhaps most importantly, practicing cardiologists and emergency medicine physicians could greatly benefit from such a device as it could augment (or even replace) standard electrocardiography in settings where the rapid diagnosis of myocardial ischemia (or the lack thereof) is required for proper clinical decision-making. A multi-channel, high-frequency QRS electrocardiograph is currently under development in the Life Sciences Research Laboratories at JSC. Specifically the project consisted of writing software code, some of which contained specially-designed digital filters, which will be incorporated into an existing commercial software program that is already designed to collect, plot and analyze conventional 12-lead ECG signals on a desktop, portable or palm PC. The software will derive the high-frequency QRS signals, which will be analyzed (in numerous ways) and plotted alongside of the conventional ECG signals, giving the PC-viewing clinician advanced diagnostic information that has never been available previously in all 12 ECG leads simultaneously. After the hardware and software for the advanced digital ECG monitor have been fully integrated, plans are to use the monitor to begin clinical studies both on healthy subjects and on patients with known coronary artery disease in both the outpatient and hospital settings. The ultimate goal is to get the technology out into the clinical world, where it has the potential to save lives.

DePalma, Jude L.↗
NASA NTRS2004

Intelligent Medical Systems for Aerospace Emergency Medical Services

The purpose of this project is to develop a portable, hands free device for emergency medical decision support to be used in remote or confined settings by non-physician providers. Phase I of the project will entail the development of a voice-activated device that will utilize an intelligent algorithm to provide guidance in establishing an airway in an emergency situation. The interactive, hands free software will process requests for assistance based on verbal prompts and algorithmic decision-making. The device will allow the CMO to attend to the patient while receiving verbal instruction. The software will also feature graphic representations where it is felt helpful in aiding in procedures. We will also develop a training program to orient users to the algorithmic approach, the use of the hardware and specific procedural considerations. We will validate the efficacy of this mode of technology application by testing in the Johns Hopkins Department of Emergency Medicine. Phase I of the project will focus on the validation of the proposed algorithm, testing and validation of the decision making tool and modifications of medical equipment. In Phase 11, we will produce the first generation software for hands-free, interactive medical decision making for use in acute care environments.

Epler, John↗
NASA NTRS2002

Goal-Directed Ultrasound for the Diagnosis of Long-Bone Fractures by Crew Medical Officer Analogs

Current construction activities on-board the International space Station (ISS) may increase fracture incidence in space, and ultrasound (US) is the only tm-board diagnostic imaging capability. The clinical utility of US in identifying long-bone fractures is un1aowe, particularly using non-radiologist operators. We sought to determine the accuracy of US in identifying fractures of the humerus and femur, as performed by emergency medicine physicians and surgeons with minimal experience in ultrasound image acquisition and interpretation, after a standardized taming session.

Marshburn, Thomas H.↗
NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗