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NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗

NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗

NASA NTRS2009

Kennedy's Biomedical Laboratory Makes Multi-Tasking Look Easy

If it is one thing that Florida has in abundance, it is sunshine and with that sunshine heat and humidity. For workers at the Kennedy Space Center that have to work outside in the heat and humidity, heat exhaustion/stroke is a real possibility. It might help people to know that Kennedy's Biomedical Laboratory has been testing some new Koolvests(Trademark) that can be worn underneath SCAPE suits. They have also been working on how to block out high noise levels; in fact, Don Doerr, chief of the Biomedical Lab, says, "The most enjoyable aspect is knowing that the Biomedical Lab and the skills of its employees have been used to support safe space flight, not only for the astronaut flight crew, but just as important for the ground processing personnel as well." The NASA Biomedical Laboratory has existed in the John F. Kennedy's Operations and Checkout Building since the Apollo Program. The primary mission of this laboratory has been the biomedical support to major, manned space programs that have included Apollo, Apollo-Soyuz, Skylab, and Shuttle. In this mission, the laboratory has been responsible in accomplishing much of the technical design, planning, provision, fabrication, and maintenance of flight and ground biomedical monitoring instrumentation. This includes the electronics in the launch flight suit and similar instrumentation systems in the spacecraft. (Note: The Lab checked out the system for STS-128 at Pad A using Firing room 4 and ground support equipment in the lab.) During Apollo, there were six engineers and ten technicians in the facility. This has evolved today to two NASA engineers and two NASA technicians, a Life Science Support contract physiologist and part-time support from an LSSC nurse and physician. Over the years, the lab has enjoyed collaboration with outside agencies and investigators. These have included on-site support to the Ames Research Center bed rest studies (seven years) and the European Space Agency studies in Toulouse, France (two years). The lab has also actively collaborated with the US Army Institute for Surgical Research, the USAF School of Aerospace Medicine, and the USN Naval Experimental Diving Unit. Because the lab often evaluates various forms of commercial-off-the-shelf life support equipment, the laboratory works closely with private companies, both domestic and foreign. The European companies seem to be more proactive and participatory with the advancement of personal protective equipment. Because these companies have viewed the space program's unique need for advanced forms of personal protective equipment, some have responded with new designs based on the prediction that these advances will soon find markets in the commercial sector. Using much of the same skills and equipment, the laboratory also addresses physiological testing of humans by supporting flight experiments and personnel involved with ground processing. While Johnson Space Center is primarily responsible for flight experiments, the Kennedy's Biomedical Lab provides the local support. However, as stated above, there are many challenges facing KSC workers that gain the attention of this lab in the measurement of the problem and the selection and testing of countermeasures. These include respiratory protection, whole body suits, hearing protection and heat stress, among many others.

Dunn, Carol Anne↗

NASA NTRS2016

Modularization and Validation of FUN3D as a CREATE-AV Helios Near-Body Solver

Under a recent collaborative effort between the US Army Aeroflightdynamics Directorate (AFDD) and NASA Langley, NASA's general unstructured CFD solver, FUN3D, was modularized as a CREATE-AV Helios near-body unstructured grid solver. The strategies adopted in Helios/FUN3D integration effort are described. A validation study of the new capability is performed for rotorcraft cases spanning hover prediction, airloads prediction, coupling with computational structural dynamics, counter-rotating dual-rotor configurations, and free-flight trim. The integration of FUN3D, along with the previously integrated NASA OVERFLOW solver, lays the ground for future interaction opportunities where capabilities of one component could be leveraged with those of others in a relatively seamless fashion within CREATE-AV Helios.

Jain, Rohit↗

NASA NTRS2008

NASA and Army Collaboration on Unmanned Systems Presentation to the Association for Unmanned Vehicle Systems International (AUVSI)

This viewgraph presentation describes the collaborative effort of NASA and the US Army on unmanned systems. The contents include: 1) Robotic/Autonomous Systems Architecture Development; 2) Synergy In Robotics/Autonomous Systems Development; 3) Surface Mobility Systems: Lunar Pylon Network Project; 4) Lunar Pylon Network Enables Multiple Vehicle Operations & Logistics; 5) Surface Mobility Systems: MARCbot IV-N Project Overview; 6) Autonomous Logistics Support Demonstration; and 7) Lunar Network Demonstration and Collaborative Effort.

Fernandez, Ken↗

NASA NTRS2011

Psychophysiological Studies in Extreme Environments

This paper reviews the results from two studies that employed the methodology of multiple converging indicators (physiological measures, subjective self-reports and performance metrics) to examine individual differences in the ability of humans to adapt and function in high stress environments. The first study was a joint collaboration between researchers at the US Army Research Laboratory (ARL) and NASA Ames Research Center. Twenty-four men and women active duty soldiers volunteered as participants. Field tests were conducted in the Command and Control Vehicle (C2V), an enclosed armored vehicle, designed to support both stationary and on-the-move operations. This vehicle contains four computer workstations where crew members are expected to perform command decisions in the field under combat conditions. The study objectives were: 1) to determine the incidence of motion sickness in the C2V relative to interior seat orientation/position, and parked, moving and short-haul test conditions; and 2) to determine the impact of the above conditions on cognitive performance, mood, and physiology. Data collected during field tests included heart rate, respiration rate, skin temperature, and skin conductance, self-reports of mood and symptoms, and cognitive performance metrics that included seven subtests in the DELTA performance test battery. Results showed that during 4-hour operational tests over varied terrain motion sickness symptoms increased; performance degraded by at least 5 percent; and physiological response profiles of individuals were categorized based on good and poor cognitive performance. No differences were observed relative to seating orientation or position.

Toscano, William B.↗

NASA NTRS2021

Montana Water Resources II: Enhancing a Moisture Index for Drought and Flood Monitoring in the Missouri River Basin

The Missouri River Basin is a major global breadbasket, containing large amounts of agricultural land. Recent devastating weather events have motivated regional organizations to dedicate efforts to drought and flood monitoring and early warning systems. The DEVELOP team partnered with the following organizations: Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, NOAA Regional Climate Services of the Central Region, NOAA Physical Sciences Laboratory, and the US Army Corps of Engineers' Missouri River Basin Water Management Division. The team collaborated with the partners in their efforts to monitor flood and drought by enhancing a composite moisture index (CMI) for the Missouri River Basin. The CMI leverages NASA Earth observations to derive snow cover data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) mission and snow water equivalent and snowdepth datasets from the NOAA NWS National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS). Building upon this framework, the team added groundwater storage data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, soil moisture data from Soil Moisture Active Passive (SMAP), and United States Geological Survey in situ streamflow data to the CMI. To test the tool’s validity, the team compared the CMI results to historically extreme dry and extreme wet years, March 2017 and March 2019, respectively. The CMI accurately reflects both 2017 and 2019 climate conditions in the Missouri River Basin. The refined CMI enhances the understanding of antecedent soil moisture conditions and improves flood and drought forecasting in the Missouri River Basin before the growing season.

Chloe Schneider↗

NASA NTRS2000

Experimental Analysis of Mast Lifting and Bending Forces on Vibration Patterns Before and After Pinion Reinstallation in an OH-58 Transmission Test Rig

As part of a collaborative research program between NASA Ames Research Center (ARC), NASA Glenn Research Center (GRC), and the US Army Laboratory, a series of experiments is being performed in GRC's 500 HP OH-58 Transmission Test Rig facility and ARC's AH-I Cobra and OH-58c helicopters. The findings reported in this paper were drawn from Phase-I of a two-phase test-rig experiment, and are focused on the vibration response of an undamaged pinion gear operating in the transmission test rig. To simulate actual flight conditions, the transmission system was run at three torque levels, as well as two mast lifting and two mast bending levels. The test rig was also subjected to disassembly and reassembly of the main pinion housing to simulate the effect of maintenance operations. An analysis of variance based on the total power of the spectral distribution indicates the relative effect of each experimental factor, including Wong interactions with torque. Reinstallation of the main pinion assembly is shown to introduce changes in the vibration signature, suggesting the possibility of a strong effect of maintenance on HUMS design and use. Based on these results, further research will be conducted to compare these vibration responses with actual OH58c helicopter transmission vibration patterns.

Huff, Edward M.↗

NASA NTRS2018

Transboundary Water: Improving Methodologies and Developing Integrated Tools to Support Water Security

River basins for which transboundary coordination and governance is a factor are of concern to US national security, yet there is often a lack of sufficient data-driven information available at the needed time horizons to inform transboundary water decision-making for the intelligence, defense, and foreign policy communities. To address this need, a two-day workshop entitled Transboundary Water: Improving Methodologies and Developing Integrated Tools to Support Global Water Security was held in August 2017 in Maryland. The committee that organized and convened the workshop (the Organizing Committee) included representatives from the National Aeronautics and Space Administration (NASA), the US Army Corps of Engineers Engineer Research and Development Center (ERDC), and the US Air Force. The primary goal of the workshop was to advance knowledge on the current US Government and partners' technical information needs and gaps to support national security interests in relation to transboundary water. The workshop also aimed to identify avenues for greater communication and collaboration among the scientific, intelligence, defense, and foreign policy communities. The discussion around transboundary water was considered in the context of the greater global water challenges facing US national security.

Hakimdavar, Raha↗