# Reconstructing strontium-90 intake in beagles using neural networks: a data-driven assessment of historical inhalation records

## TL;DR

This paper uses machine learning to estimate how much strontium-90 dogs inhaled based on historical data, improving accuracy despite data limitations.

## Contribution

The study introduces neural networks to reconstruct strontium-90 intake in beagles using historical bioassay data with individual-specific features.

## Key findings

- Neural networks can estimate strontium-90 intake from bioassay data within 14 days post-exposure.
- Summary statistics from historical records lack resolution for individualized ML modeling.
- Historical dose estimates can serve as surrogate features when multiple time points are available.

## Abstract

Dose estimation in response to internal radionuclide exposures requires reconstruction of the initial intake activity, which is frequently unknown due to the absence of a priori data. In such scenarios, intake is inferred from bioassay measurements obtained at one or more time points post-exposure. Reconstructing an initial intake from bioassay relies on biokinetic models that describe the body distribution and clearance of the toxicant. These models typically employ first-order differential equations with generalised population parameters, which do not capture individual variation in metabolism or anatomy. Thus, reconstruction of initial intakes is affected by multiple sources of stochasticity, including physical deposition of the inhaled radionuclide, detection system uncertainty, and inter-individual physiological variability. The capacity of machine learning (ML) algorithms to model highly non-linear and often stochastic processes makes them appropriate for augmenting intake reconstruction. This study applies artificial neural networks to estimate the initial intake activity of 90Sr inhaled by beagles. Model performance and sensitivity to input data quality were assessed through inclusion of individual-specific features, such as age, weight, and sex. Three data regimens were systematically designed, each with distinct pre-processing pipelines and model complexity. The first regimen demonstrates feasibility of intake reconstruction using bioassay measurements taken within 14 days post-exposure. The second regimen demonstrates that summary statistics of retention functions in historical records lack sufficient resolution for individualised ML modelling. The third regimen shows that historical dose estimates, despite limitations in resolution and methodology, can be used as surrogate features when multiple post-exposure time points are available. Root mean squared error was used to evaluate prediction error, while a custom metric, the variance relative difference, quantified model bias. In addition to evaluating predictive performance, this study assesses the integrity and usability of historical data from 90Sr beagle inhalation experiments conducted at the Inhalation Toxicology Research Institute between 1966 and 1987.