Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “diagnostic procedures”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

The CapiSorb Visible System (CVS) Demonstrations on ISS

Falling liquid film amine sorbent reactors have been successfully employed to scrub CO 2 aboard submarines for decades. However, applying such proven methods aboard orbiting and coast spacecraft is significantly challenged by the nearly weightless environment, where liquid sprays and films do not fall, and vapor bubbles and gases do not rise. The Capillary Sorbent (CapiSorb) Visible System (CVS) is a technology demonstration experiment performed aboard the ISS April 18 – 21, 2023. The system establishes stable steady thin liquid film flows in Contactor (absorber) and Degasser (desorber/stripper) replacing the passive role of gravity with the combined passive roles of surface tension, wetting, and system geometry. A TOX-0 fructose ersatz liquid sorbent is employed enabling ‘transparent’ experiments performed and filmed by the crew safely in the open cabin of the ISS. Completed objectives include demonstrations of stable passive ‘massively’ parallel planar thin film capillary flows across atmospheric pressure Contactor and sealed heated Degasser. The impacts of varying flow rate, flow direction, heat input, viscosity, positive and negative Degasser pressures, condensate collection and return, fluid distribution, interfacial stability, and others are reported. At least 49 diagnostics are recorded for digitization and subsequent thermal-fluids model validation by a single HD video downlink during the nearly 22 hours of operations. An overview of the flight hardware including description of the components, diagnostics, crew procedures, flight operations, and summary of accomplishments is reported in Ref. 1. Further details of the diagnostics, tests performed, and data reduction is reported in Ref. 2. This report collects both1,2 into a single report adding methods of data digitization, reduction, and archive along with analyses and discussions of technology impacts.

microgravity↗
DOE OSTI2020

Moly 99 Reactor Case Study

Molybdenum-99 (Moly 99) is a critical raw material for Technetium (Tc) 99m, a radioactive isotope most widely used in nuclear medicine procedures. Moly 99 has a short half-life of about six hours, which means it cannot be stockpiled. When Moly 99 decays, it turns into Tc 99m, which has a half-life of 214,000 years. The photon energy emitted from the decay of Moly 99 is used in a variety of nuclear imaging technologies such as gamma cameras. Furthermore, radiopharmaceutical manufacturers use the photon energy emitted from the decay of Moly 99 to produce generators for hospitals, clinics, and radiopharmacies. Once Moly 99 decays to Tc 99m, it is used to make individual patient doses for a variety of diagnostic imaging procedures. The Moly 99 Reactor Design is a conceptual blueprint for Moly 99 production that does not use weapon-grade uranium; instead, the reactor has a target core of low-enriched uranium. The reactor design is small, reaching a foot-and-a-half in height and diameter and consumes less than two megawatts of power. The reactor's only purpose is for medical isotope production and with every fission, Moly 99 is produced.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Review of Doses for Dental Imaging in 2010–2020 and Development of a Web Dose Calculator

Dental imaging is one of the most common types of diagnostic radiological procedures in modern medicine. We introduce a comprehensive table of organ doses received by patients in dental imaging procedures extracted from literature and a new web application to visualize the summarized dose information. We analyzed articles, published after 2010, from PubMed on organ and effective doses delivered by dental imaging procedures, including intraoral radiography, panoramic radiography, and cone-beam computed tomography (CBCT), and summarized doses by dosimetry method, machine model, patient age, and technical parameters. Mean effective doses delivered by intraoral, 1.32 (0.60–2.56) μSv, and panoramic, 17.93 (3.47–75.00) μSv, procedures were found to be about1% and 15% of that delivered by CBCT, 121.09 (17.10–392.20) μSv, respectively. In CBCT imaging, child phantoms received about 29% more effective dose than the adult phantoms received. The effective dose of a large field of view (FOV) (>150 cm2) was about 1.6 times greater than that of a small FOV (<50 cm2). The maximum CBCT effective dose with a large FOV for children, 392.2 μSv, was about 13% of theeffective dose that a person receives on average every year from natural radiation, 3110 μSv. Monte Carlo simulations of representative cases of the three dental imaging procedures were then conducted to estimate and visualize the dose distribution within the head. The user-friendly interactive web application (available at http://dentaldose.org) receives user input, such as the number of intraoral radiographs taken, and displays total organ and effective doses, dose distribution maps, and a comparison with other medical and natural sources of radiation. The web dose calculator provides a practical resource for patients interested in understanding the radiation doses delivered by dental imaging procedures.

Lee, Hawon↗
DOE OSTI2022

Intracardiac Electrical Imaging using the 12-lead ECG: A Machine Learning Approach using Synthetic Data

Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

The NASTRAN User's Manual (Level 15)

The User's manual for the NASA Structural Analysis (NASTRAN) program is presented. The manual contains all information needed to solve problems with NASTRAN. The volume is instructional and encyclopedic. The manual includes instruction in structural modeling techniques, instruction in input preparation, and information to assist the interpretation of the output. Descriptions of all input data cards, restart procedures, and diagnostic messages are developed.

Mccormick, C. W.↗
NASA NTRS1983

A study of interior noise levels, noise sources and transmission paths in light aircraft

The interior noise levels and spectral characteristics of 18 single-and twin-engine propeller-driven light aircraft, and source-path diagnosis of a single-engine aircraft which was considered representative of a large part of the fleet were studied. The purpose of the flight surveys was to measure internal noise levels and identify principal noise sources and paths under a carefully controlled and standardized set of flight procedures. The diagnostic tests consisted of flights and ground tests in which various parts of the aircraft, such as engine mounts, the engine compartment, exhaust pipe, individual panels, and the wing strut were instrumented to determine source levels and transmission path strengths using the transfer function technique. Predominant source and path combinations are identified. Experimental techniques are described. Data, transfer function calculations to derive source-path contributions to the cabin acoustic environment, and implications of the findings for noise control design are analyzed.

Hayden, R. E.↗
NASA NTRS1990

An integrated approach to system design, reliability, and diagnosis

The requirement for ultradependability of computer systems in future avionics and space applications necessitates a top-down, integrated systems engineering approach for design, implementation, testing, and operation. The functional analyses of hardware and software systems must be combined by models that are flexible enough to represent their interactions and behavior. The information contained in these models must be accessible throughout all phases of the system life cycle in order to maintain consistency and accuracy in design and operational decisions. One approach being taken by researchers at Ames Research Center is the creation of an object-oriented environment that integrates information about system components required in the reliability evaluation with behavioral information useful for diagnostic algorithms. Procedures have been developed at Ames that perform reliability evaluations during design and failure diagnoses during system operation. These procedures utilize information from a central source, structured as object-oriented fault trees. Fault trees were selected because they are a flexible model widely used in aerospace applications and because they give a concise, structured representation of system behavior. The utility of this integrated environment for aerospace applications in light of our experiences during its development and use is described. The techniques for reliability evaluation and failure diagnosis are discussed, and current extensions of the environment and areas requiring further development are summarized.

Patterson-Hine, F. A.↗
NASA NTRS2001

Cybersonics: Tapping into Technology

With the assistance of Small Business Innovation Research (SBIR) funding from NASA's Jet Propulsion Laboratory, Cybersonics, Inc., developed an ultrasonic drill with applications ranging from the medical industry to space exploration. The drill, which has the ability to take a core sample of the hardest granite or perform the most delicate diagnostic medical procedure, is a lightweight, ultrasonic device made to fit in the palm of the hand. Piezoelectric actuators, which have only two moving parts and no gears or motors, drive the components of the device, enabling it to operate in a wide range of temperatures. The most remarkable aspect of the drill is its ability to penetrate even the hardest rock with minimal force application. The ultrasonic device requires 20 to 30 times less force than standard rotating drills, allowing it to be safely guided by hand during operation. Also, the drill is operable at a level as low as three watts of power, where conventional drills require more than three times this level. Potential future applications for the ultrasonic drill include rock and soil sampling, medical procedures that involve core sampling or probing, landmine detection, building and construction, and space exploration. Cybersonics, Inc. developed an ultrasonic drill with applications ranging from the medical industry to space exploration.

Source record↗
NASA NTRS1998

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Patterson-Hine, Ann↗
NASA NTRS2004

Chronic Medication and or Monitoring for Long Duration Space Flight: What Should The Policy Be?

While a private individual with a chronic medical condition could perhaps justifiably claim the right to become a "space tourist" through informed consent, NASA and other International crew members are national assets, trained at government expense to fulfill a mission considered essential to national goals. The medical requirements/policy for selection and certification of crewmembers for long duration missions to the International Space Station (ISS) should address: (1) Crewmembers with chronic conditions that require regular dosing of life-sustaining medication for the purposes of secondary or tertiary prevention (i.e. tumor suppression therapy or anticoagulation). (2) Crew members with conditions that will be worsened by spaceflight exposure, placing them at additional personal health risk though not necessarily increasing mission risk (i.e. previous radiation exposure or preflight osteopenia/osteoporosis) The following policies are recommended. A crewmember should not be assigned to an International Space Station mission who: (3) Requires medications to sustain life or who has a disease, which requires medical procedures or diagnostics to monitor and treat same. (4) Has medical condition, which would prevent participation as a candidate in many scientific and medical operational research studies, as their underlying condition would confound the collected data. (5) Requires specialized medical devices for monitoring or treatment of a medical condition, including conditions that require special provisions to conduct routine required countermeasure activities. (6) Who has a disease or requires treatment for same, and for which the effects of space travel may be deleterious to the short or long term health of the individual, An evidence-based approach will be used to provide rationale for policy recommendations under these scenarios. Operational impacts of allowing these medical conditions to fly on the ISS will also be presented.

Hamilton, Douglas R.↗
NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗
NASA NTRS2018

Automated Semantic Segmentation for Volumetric Cardiovascular Feature Quantification and Pathology Assessment

We present a pipeline method that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification as a fully automatic processing pipeline. The initial element consists of a 2D U-Net convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The results of the segmentation were used to compute a comprehensive volumetric feature matrix that captured diagnostic clinical procedure data and that was used to model a cardiac pathology classifier.Our approach evaluated anonymized parasternal MRI cardiac images from a database of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. We achieved top average Dice index scores of 0.939, 0.849, 0.886 for structure segmentation of the left ventricle (LV), right ventricle (RV) and myocardium respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using our trained model.

Lindsey, Tony↗
NASA NTRSDate not supplied

The Evolution of the Impact Evidence Library Methods – What We Learned

During fiscal years 2020-2022 NASA’s Human Research Program, Exploration Medical Capability Element has worked to source this evidence for the model. During the process several lessons learned have been identified and drive recommendations for future efforts. The ICL 1.0: Spaceflight Medical Operational expertise should be consulted regarding the addition of future conditions. A clinician experienced with the nuances of spaceflight is recommended to have a minimum of 20 hours per condition to adequately source the evidence. Condition definitions should be carefully crafted to maintain mutual exclusivity. Common side effects from medications, common and consequential incidental findings in laboratory studies, and consequential side effects of diagnostic and procedural capabilities should be considered for inclusion as unique conditions in future iterations of the ICL 1.0. Incidence: Spaceflight incidence, although based on small population sizes, is the gold standard. Terrestrial incidence should be applied cautiously. Probability of minor vs. severe cases of the condition: Data informing severity probability of the medical condition are nearly always terrestrial. Special attention should be paid to this metric as it drives outcomes in the model and terrestrial data often overestimate severity of expected cases in spaceflight. Condition duration: Three clinical phases currently exist in the model. They represent the time necessary for diagnosis, treatment/convalescence/recurrence, and the remaining duration of the mission after maximal recovery, respectively. Further subdivision of acute and convalescent treatment/recurrence is recommended. Probability of need for evacuation: Surrogates for “Return to Definitive Care” were chosen prior to data collection. In most cases these surrogates were “need for hospitalization” or “need for surgery.” For many conditions these may not be realistic drivers of “Return to Definitive Care” and should be re-examined. Probability of Crew Mortality: Terrestrial mortality data is difficult to apply to astronauts who, invariably, have less comorbidities than terrestrial subjects. It must be applied judiciously. The proportion of mission tasks impaired by the condition: “Task Impairment” was assigned to each condition prior to the condition duration data becoming available. Future efforts should assign Task Impairment as the final step in sourcing the evidence.

A.J. Kreykes↗
NASA NTRSDate not supplied

Crew Medical Training to Progressively Enable EIMO

Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.

Jay Lemery↗
DOE OSTI2021

Methods and compositions for protection of cells and tissues from computed tomography radiation

Described are methods for preventing or inhibiting genomic instability and in cells affected by diagnostic radiology procedures employing ionizing radiation. Embodiments include methods of preventing or inhibiting genomic instability and in cells affected by computed tomography (CT) radiation. Subjects receiving ionizing radiation may be those persons suspected of having cancer, or cancer patients having received or currently receiving cancer therapy, and or those patients having received previous ionizing radiation, including those who are approaching or have exceeded the recommended total radiation dose for a person.

Grdina, David J.↗
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↗