Machine Learning Reconstruction of Intake Activity from Historical 90-Sr Inhalation Beagle and 238-Pu Injection Mice Datasets

Computational internal dosimetry is a multifaceted chapter of health physics that operates at the intersection of systemic biokinetic models that describe the intake and time-varying biodistribution of radionuclides in the body, which in turn informs internal dose estimates to assess health risks. Biokinetic models, primarily published by the International Commission on Radiological Protection (ICRP), are standard compartment models that may be encapsulated by systems of first-order differential equations, whose constants (i.e., transfer coefficients, which then give rise to dose coefficients) are published for different age groups. In the case of a radiological accident, these models are invaluable for reconstructing the intake activity for individuals and the subsequent internal dose estimate. However, the intake reconstructions and dose estimates that are made from these values may lack adequate individualization, which is a consequence of the inherently high biological variation seen among different persons. Efforts towards the stochastic sampling of biokinetic parameters aid in this shortcoming, however, the present study implements machine learning (ML) to simultaneously investigate the data that would be needed to accurately reconstruct intake activity and the limitations of machine learning in refining current and future biokinetic models. Machine learning is a subset of artificial intelligence that excels in uncovering complex, and often highly non-linear, patterns in data through the use of advanced statistical methods, thus, it lends well to the highly uncertain physical system modelled by biokinetics and investigated through bioassay. The study implements two distinct datasets of internal exposures and two forms of machine learning (neural networks and random forests). The first dataset comes from historical experimentation performed by the Inhalation Toxicology Research Institute (ITRI) on beagles inhaling a soluble form of Strontium-90. The second is an aggregation of contemporary studies that all saw mice injected with known activities of Plutonium-238. Overall, the present study implements various data preprocessing techniques, augmentations, and simulations to drive cohorts of ML models which inform data quality/needs while highlighting the limitations of ML to assist in this consequential field of health physics.