Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features
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Presentation to be submitted to and presented during the Health Physics Society (HPS) Annual Meeting 2023, July 23–27, 2023, National Harbor, MD (http://burkclients.com/hps/2023AM/site/index.html).
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News highlight intended for a new publication, the "LDRD Quarterly Highlights." The quarterly newsletter contains highlights from LDRD and SDRD programs and will be published on a LANL-hosted website. This story was previously featured on the NNSS.gov website
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This is a presentation that will be given at the International Conference on the Development of Preparedness for National and International Emergency Response (EPR2021), 11–15 October 2021, in Vienna, Austria.
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Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.
Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.
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Unlike short-term laboratory experiments, studies at sites historically contaminated with radionuclides can provide insight into contaminant migration behavior at environmentally-relevant decadal timescales. One such site is Pond B, a seasonally stratified reservoir within Savannah River Site (SC, USA) has low levels (μBq L –1 ) of plutonium in the water column. Here, we evaluate the origin of plutonium using high-precision isotope measurements, investigate the impact of water column geochemistry on plutonium cycling during different stratification periods, and re-evaluate long-term mass balance of plutonium in the pond. New isotopic data confirm that reactor-derived plutonium overwhelms input from Northern Hemisphere fallout at this site. Two suggested mechanisms for observed plutonium cycling in the water column include: (1) reductive dissolution of sediment-derived Fe(III)-(oxyhydr)oxides during seasonal stratification and (2) plutonium stabilization complexed strongly to Fe(III)-particulate organic matter (POM) complexes. While plutonium may be mobilized to a limited extent by stratification and reductive dissolution, peak plutonium concentrations are in shallow waters and associated with Fe(III)-POM at the inception of stratification. This suggests that plutonium release from sediments during stratification is not the dominant mechanism driving plutonium cycling in the pond. Importantly, our analysis suggests that the majority is retained in shallow sediments and may become increasingly recalcitrant.