Results for “Health Behavior” — Page 23

NASA NTRS2005

Obesity-related cardiovascular risk factors: intervention recommendations to decrease adolescent obesity

The incidence of adolescent obesity is increasing dramatically in the United States with associated risks of hypertension, adverse lipid profiles, and Type II diabetes. Unless reversed, this trend predicts an epidemic of adult cardiovascular disease. Interventions at home, at school, and in the community are required to empower teens to increase physical activity and to modify eating habits. This article describes assessment for obesity-related health problems as well as scientific guidelines and research-based intervention strategies to decrease obesity in adolescents.

Review↗

NASA NTRS1996

Aspects of the Development of Housing for the Spaceflight of Pregnant and Lactating Rats with Neonates

Recent and upcoming spaceflights are investigating the effect of weightlessness on developing neural and organ systems. Pregnant rats and dams with neonates have to be accommodated in cages that support the special requirements of these animals. Extensive ground testing of cage concepts, the effect of launch and landing stresses on the maintenance of pregnancy and maternal behavior at different neonatal ages, and techniques for monitoring adaptability to change are discussed. A spaceflight opportunity for the NlH.R3 payload of rat families at three different postnatal ages demonstrated that the survival of very young animals was not good but that older newborns could be returned to Earth in reasonably good health. The development of cages for the Research Animal Holding Facility (RAHF) to support the flight of neonates on Neurolab was continued and incorporated modifications that were demonstrated by the NIH.R3 flight. Other modifications to the RAHF are discussed. Data from biocompatibility and experiment verification testing are presented.

Hinds, William E.↗

NASA NTRSDate not supplied

Impact of the ISS Environment on CNS in Drosophila Melanogaster

Altered gravity and elevated carbon dioxide (CO 2 ) levels as experienced on the ISS can adversely affect human health across various organ systems, especially the Central Nervous System (CNS). Investigating these changes is essential for understanding the long-term effects of spaceflight on human physiology to ensure crew health. Ground-based analogs provide an efficient method to evaluate alterations induced by chronic spaceflight on a larger scale. This study focuses on CNS changes in response to hypergravity (HG) and elevated CO 2 levels via a ground-based analog using the well-established model organism, Drosophila melanogaster. We hypothesize behavior and physiological changes immediately post exposure to HG+CO 2 , along with chronic effects up to 25 days post-exposure. Adult male and female flies were exposed to varying gravity loads (1g, 1.2g, and 3g) and elevated CO 2 levels (~4000 ppm, mimicking CO 2 levels on the ISS) for 15 days and were assessed immediately and at 5-, 10-, and 25-days post-exposure, mirroring astronauts’ post-return profiling. The flies were assessed for neurobehavioral changes, including longevity and negative geotaxis; brain morphological changes, such as dopaminergic neuron count, apoptosis, and glial cell density; and bioenergetic changes in the brain, including mitochondrial abundance and membrane potential. Longevity remained unchanged under hypergravity, even with the addition of elevated CO 2 as a stressor. However, despite the stable lifespan, quality of life appeared to be affected, as shown by negative geotaxis and neuroanatomical changes. Negative geotaxis testing revealed a reduction in motor ability at R0 across all conditions, which correlated with a decrease in dopaminergic (DA) neuron count. Additional observations suggested further systemic alterations, including a decrease in glial cell count, an increase in apoptosis, and notable bioenergetic changes. Collectively, these findings contribute to our understanding of the long-term effects of spaceflight on the CNS.

spaceflight↗

NASA NTRS1997

Real-Time Sensor Validation System Developed for Reusable Launch Vehicle Testbed

A real-time system for validating sensor health has been developed for the reusable launch vehicle (RLV) program. This system, which is part of the propulsion checkout and control system (PCCS), was designed for use in an integrated propulsion technology demonstrator testbed built by Rockwell International and located at the NASA Marshall Space Flight Center. Work on the sensor health validation system, a result of an industry-NASA partnership, was completed at the NASA Lewis Research Center, then delivered to Marshall for integration and testing. The sensor validation software performs three basic functions: it identifies failed sensors, it provides reconstructed signals for failed sensors, and it identifies off-nominal system transient behavior that cannot be attributed to a failed sensor. The code is initiated by host software before the start of a propulsion system test, and it is called by the host program every control cycle. The output is posted to global memory for use by other PCCS modules. Output includes a list indicating the status of each sensor (i.e., failed, healthy, or reconstructed) and a list of features that are not due to a sensor failure. If a sensor failure is found, the system modifies that sensor's data array by substituting a reconstructed signal, when possible, for use by other PCCS modules.

Jankovsky, Amy L.↗

NASA NTRS1998

Overview of the Neurolab Spacelab mission

Neurolab is a NASA Spacelab mission with multinational cooperative participation that is dedicated to research on the nervous system. The nervous systems of all animal species have evolved in a one-g environment and are functionally influenced by the presence of gravity. The absence of gravity presents a unique opportunity to gain new insights into basic neurologic functions as well as an enhanced understanding of physiological and behavioral responses mediated by the nervous system. The primary goal of Neurolab is to expand our understanding of how the nervous system develops, functions in, and adapts to microgravity space flight. Twenty-six peer reviewed investigations using human and nonhuman test subjects were assigned to one of eight science discipline teams. Individual and integrated experiments within these teams have been designed to collect a wide range of physiological and behavior data in flight as well as pre- and postflight. Information from these investigations will be applicable to enhancing the well being and performance of future long duration space travelers, will contribute to our understanding of normal and pathological functioning of the nervous system, and may be applied by the medical community to enhance the health of humans on Earth.

manned↗

NASA NTRS2005

Space Based Ornithology: On the Wings of Migration and Biophysics

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Migration is an incredibly diverse and complex behavior. A broad outline of space based research must address three fundamental questions: (1) where could birds be, i.e. what is their fundamental niche constrained by their biophysical limits? (2) where do we actually find birds, i.e. what is their realizable niche as modified by local or regional abiotic and biotic factors, and (3) how do they get there (and how do we know?), that is what are their migration patterns and associated mechanisms? Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Dynamic state variable modeling provides one tool for studying bird migration across multiple scales and can be linked to mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. Further these models provide an ecological forecasting tool for science and application users to address what are the possible consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Smith, James A.↗

NASA NTRS2007

Strain Measurement Using FBG on COPV in Stress Rupture Test

White Sands Test Facility (WSTF) was requested to perform ambient temperature hydrostatic pressurization testing of a Space Transportation System (STS) 40-in. Kevlar Composite Overwrapped Pressure Vessel (COPV). The 40-in. vessel was of the same design and approximate age as the STS Main Propulsion System (MPS) and Orbiter Maneuvering System (OMS) vessels. The NASA Engineering Safety Center (NESC) assembled a team of experts and conducted an assessment that involved a review of national Kevlar COPY data. During the review, the STS COPVs were found to be beyond their original certification of ten years. The team observed that the likelihood of STS COPV Stress rupture, a catastrophic burst before leak failure mode, was greater than previously believed. Consequently, a detailed assessment of remaining stress rupture life became necessary. Prior to STS-114, a certification deviation was written for two flights of OV-103 (Discovery) and OV-104 (Atlantis) per rationale that was based on an extensive review of the Lawrence Livermore National Laboratories, COPV data, and revisions to the STS COPV stress levels. In order to obtain flight rationale to extend the certification deviation through the end of the Program, the Orbiter Project Office has directed an interagency COPV team to conduct further testing and analysis to investigate conservatism in the stress rupture model and evaluate material age degradation. Additional analysis of stress rupture life requires understanding the fiber stresses including stress that occurs due to thru-wall composite compression in COPV components. Data must be obtained at both zero gauge pressure (pre-stress) and at the component operating pressure so that this phenomenon can be properly evaluated. The zero gauge pressure stresses are predominantly a result of the autofrettage process used during vessel manufacture. Determining these pre-stresses and the constitutive behavior of the overwrap at pressure will provide necessary information to better predict the remaining life of the STS COPVs. The primary test objective is obtaining data to verify the hypothesis of a radially oriented thru-thickness stress-riser in the COPV composite whose magnitude is a function of the applied pressure and the load history. The anticipated load dependent response follows from the constitutive behavior of the composite overwrap so data to quantify its nonlinear and time dependent response will be sought. The objective of the Fiber Braggs Gratings (FBGs) were to advance the state-of-the-art by developing techniques using FBG sensors that are capable of assessing stress-rupture degradation in Kevlar COPVs in a health monitoring mode (1). Moreover, they sought to answer questions of how embedded sensors affect overall integrity of the structure. And lastly, they sought to provide an important link in the overall stress rupture study that will help close the loop on the COPV fabrication process. NDE inspection methods will be used from start to finish and FBG will be an integral link within the overall chain.

Banks, Curtis↗

NASA NTRS2016

System and Method for Outlier Detection via Estimating Clusters

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Iverson, David J.↗

NASA NTRS2013

Spinoff 2012

Topics covered include: Water Treatment Technologies Inspire Healthy Beverages; Dietary Formulas Fortify Antioxidant Supplements; Rovers Pave the Way for Hospital Robots; Dry Electrodes Facilitate Remote Health Monitoring; Telescope Innovations Improve Speed, Accuracy of Eye Surgery; Superconductors Enable Lower Cost MRI Systems; Anti-Icing Formulas Prevent Train Delays; Shuttle Repair Tools Automate Vehicle Maintenance; Pressure-Sensitive Paints Advance Rotorcraft Design Testing; Speech Recognition Interfaces Improve Flight Safety; Polymers Advance Heat Management Materials for Vehicles; Wireless Sensors Pinpoint Rotorcraft Troubles; Ultrasonic Detectors Safely Identify Dangerous, Costly Leaks; Detectors Ensure Function, Safety of Aircraft Wiring; Emergency Systems Save Tens of Thousands of Lives; Oxygen Assessments Ensure Safer Medical Devices; Collaborative Platforms Aid Emergency Decision Making; Space-Inspired Trailers Encourage Exploration on Earth; Ultra-Thin Coatings Beautify Art; Spacesuit Materials Add Comfort to Undergarments; Gigapixel Images Connect Sports Teams with Fans; Satellite Maps Deliver More Realistic Gaming; Elemental Scanning Devices Authenticate Works of Art; Microradiometers Reveal Ocean Health, Climate Change; Sensors Enable Plants to Text Message Farmers; Efficient Cells Cut the Cost of Solar Power; Shuttle Topography Data Inform Solar Power Analysis; Photocatalytic Solutions Create Self-Cleaning Surfaces; Concentrators Enhance Solar Power Systems; Innovative Coatings Potentially Lower Facility Maintenance Costs; Simulation Packages Expand Aircraft Design Options; Web Solutions Inspire Cloud Computing Software; Behavior Prediction Tools Strengthen Nanoelectronics; Power Converters Secure Electronics in Harsh Environments; Diagnostics Tools Identify Faults Prior to Failure; Archiving Innovations Preserve Essential Historical Records; Meter Designs Reduce Operation Costs for Industry; Commercial Platforms Allow Affordable Space Research; Fiber Optics Deliver Real-Time Structural Monitoring; Camera Systems Rapidly Scan Large Structures; Terahertz Lasers Reveal Information for 3D Images; Thin Films Protect Electronics from Heat and Radiation; Interferometers Sharpen Measurements for Better Telescopes; and Vision Systems Illuminate Industrial Processes.

Source record↗

NASA NTRS2004

Inductive System Health Monitoring

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Iverson, David L.↗

NASA NTRS2007

Lunar Dust: Characterization and Mitigation

Lunar dust is a ubiquitous phenomenon which must be explicitly addressed during upcoming human lunar exploration missions. Near term plans to revisit the moon as a stepping stone for further exploration of Mars, and beyond, places a primary emphasis on characterization and mitigation of lunar dust. Comprised of regolith particles ranging in size from tens of nanometers to microns, lunar dust is a manifestation of the complex interaction of the lunar soil with multiple mechanical, electrical, and gravitational effects. The environmental and anthropogenic factors effecting the perturbation, transport, and deposition of lunar dust must be studied in order to mitigate it's potentially harmful effects on exploration systems. The same hold true for assessing the risk it may pose for toxicological health problems if inhaled. This paper presents the current perspective and implementation of dust knowledge management and integration, and mitigation technology development activities within NASA's Exploration Technology Development Program. This work is presented within the context of the Constellation Program's Integrated Lunar Dust Management Strategy. This work further outlines the scientific basis for lunar dust behavior, it's characteristics and potential effects, and surveys several potential strategies for its control and mitigation both for lunar surface operations and within the working volumes of a lunar outpost. The paper also presents a perspective on lessons learned from Apollo and forensics engineering studies of Apollo hardware.

Hyatt. Mark J.↗

NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗

NASA NTRS2019

Developing High-Throughput Organ-On-A-Chip Models to Investigate the Effects of Ionizing Radiation on the Central Nervous System

One of the main health risks in human space exploration is central nervous system (CNS) damage by ionizing radiation. Irradiation with simulated GCRs or their components, or high doses of low-LET radiation such as gamma rays, in animal models has been shown to cause neuronal damage together with glial cell activation and neuroinflammation and has been associated with prolonged cognitive and behavioral dysfunction. The extent of CNS damage in response to any insult, including ionizing radiation, is partially regulated by the blood-brain barrier (BBB), which enables immune cells to enter the CNS. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, immune responses and oxidative stress, and thus could serve as a robust CNS-specific target for countermeasure development. However, studies on BBB permeability and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we have established a high throughput 3D organ-on-a-chip system to study human CNS functions in response to ionizing radiation, with the eventual goal of adapting it to spaceflight missions. We utilized commercially available OrganoPlate system (Mimetas, Inc.) seeded with primary or induced pluripotent stem cell-derived human cells for developing 3D neuronal-astrocytic and BBB models. We investigated both immediate and delayed CNS dose responses to 0.5-1 Gy X-rays by measuring BBB permeability and morphology, and astrocyte activation. We have also quantified secreted markers of oxidative stress and cell viability. In the future, we are planning to monitor dendritic, axonal and synaptic changes in neurons, evaluate the combined exposures to simulated microgravity and ionizing radiation, and compare the responses to low and high-LET ionizing radiation. We anticipate these studies could indicate novel cellular and mechanistic targets for countermeasure developments to improve CNS functions in astronauts.

Malkani, Sherina↗

NASA NTRSDate not supplied

Space Radiation and Central Nervous System Impacts: NASA Standards and Evidence

It is well understood that large radiation localized doses to the brain cause clinically significant impacts to the central nervous system in human populations. However, the effects in adults exposed to lower doses remain unclear due to lack of data in relevant human cohorts. The impact of exposure to high-energy particles is even less understood. NASA’s Human Research Program relies heavily on model systems to characterize the impacts of the space radiation environment on the human brain and how potential changes may effect mission success and long term health and well-being. Animal, cellular, and molecular experiments implicate multiple – and possibly related – mechanisms that mediate impacts to the central nervous system in model systems including, but not limited to inflammation, immune responses, oxidative stress, metabolism, myelination, molecule transport, electrophysiology, and a variety of “omic” changes. While animal studies demonstrate potential changes across a number of cognitive and behavioral domains the direct applicability to the astronaut population remains unclear. Furthermore data access experiments and model systems can be inconsistent and dependent on multiple experimental variables indicating a clear need for robust validation. To minimize potential impacts to astronauts NASA limits dose to the CNS based on a combination of terrestrial epidemiology informed by experimental evidence in model systems. To date no recommendations have been provided by the National Committee on Radiation Protection and Measurements. This presentation will provide an overview of NASA’s current dose limits for CNS exposure to space radiation as well as highlights of the current state of evidence and ongoing research.

S Robin Elgart↗

NASA NTRS1977

Ground cloud effluent measurements during the May 30, 1974, Titan 3 launch at the Air Force eastern test range

Surface-level exhaust effluent measurements of HCl, CO, and particulates, ground-cloud behavior, and some comparisons with model predictions for the launch of a Titan 3 rocket are presented along with a limited amount of airborne sampling measurements of other cloud species (O3, NO, NOX). Values above background levels for these effluents were obtained at 20 of the 30 instrument sites; these values were lower than model predictions and did not exceed public health standards. Cloud rise rate, stabilization altitude, and volume are compared with results from previous launches.

Bendura, R. J.↗

NASA NTRSDate not supplied

High-Temperature Mechanical Tensile Testing of Unidirectional Sic/Sic Composites Using A Versatile Lamp Furnace

Silicon carbide (SiC) based ceramic matrix composites (CMCs) are of interest for high-temperature structural applications, including use in turbine engine components. To understand damage mechanisms and predict durability of these systems, in-depth knowledge of their behavior at ambient and high temperatures is necessary. A method for high temperature testing of SiC/SiC minicomposite specimens up to 1500oC in both tensile fast fracture and creep conditions to investigate their thermomechanical behavior will be presented. This method utilizes a novel radiation heat furnace, equipped with infrared lamps with ellipsoidal reflectors, to heat the specimen at the furnace center in user-defined atmospheres. The housing structure of the lamps allows for observation of the specimen during testing using optical methods and for conducting other experimental studies. The goal is to employ nondestructive health monitoring techniques in-situ, including acoustic emission (AE), electrical resistance (ER), and digital image correlation (DIC) to evaluate damage initiation and evolution in the minicomposite specimens during tensile testing. Results from mechanical testing and nondestructive techniques will be presented. Electron microscopy and X-ray micro-computed tomography were used to further characterize specimen damage after testing.

Aeronautics↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗