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Environmental Engineering
Air Pollution Modelling And Control
Machine Learning-enhanced Stochastic Uncertainty And Sensitivity Analysis Of The Icrp Human Respiratory Tract Model For An Inhaled Radionuclide.

Home
Research Domains
Engineering
Environmental Engineering
Air Pollution Modelling And Control
Machine Learning-enhanced Stochastic Uncertainty And Sensitivity Analysis Of The Icrp Human Respiratory Tract Model For An Inhaled Radionuclide.

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Assessment of the Cytotoxic and Immunomodulatory Effects of Substances in Human Precision-cut Lung Slices

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Published on: May 9, 2018

Machine learning-enhanced stochastic uncertainty and sensitivity analysis of the ICRP human respiratory tract model for an inhaled radionuclide.

Emmanuel Matey Mate-Kole¹, Sara C Howard², Ashley P Golden²

¹Nuclear and Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology, Atlanta, GA, United States of America.

Journal of Radiological Protection : Official Journal of the Society for Radiological Protection

|September 24, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a stochastic approach to the Human Respiratory Tract Model (HRTM) for inhaled radionuclides, improving dose calculations. The analysis reveals particle transport rates and alveolar deposition as key factors influencing radiation dose.

Keywords:

ICRP HRTM biokinetic models machine learning stochastic expansion

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Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

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Published on: March 11, 2021

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Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Published on: March 11, 2021

Area of Science:

Radiological Protection
Computational Biology
Nuclear Medicine

Background:

The International Commission on Radiological Protection (ICRP) developed the Human Respiratory Tract Model (HRTM) for radionuclide deposition and clearance.
Existing deterministic models may not fully capture the variability in inhaled particle behavior and resulting radiation doses.

Purpose of the Study:

To assess variability in deterministic biokinetic and dosimetry models for iodine-131 (¹³¹I) using a stochastic analysis based on the updated HRTM (ICRP Publication 130).
To develop an independent computational model for particle deposition (PD) and a stochastic dose calculator for enhanced radiation consequence management.

Main Methods:

Reconstructed the ICRP PD model into an independent computational model and validated it against reference data.

uncertainty and sensitivity analysis

Developed a stochastic radiological exposure dose calculator in Python, incorporating probability distribution functions for uncertain HRTM parameters and utilizing Latin Hypercube Sampling.

Performed sensitivity analysis using Random Forest regression and SHapley Additive exPlanations to identify influential parameters.

Main Results:

The independent PD model showed good agreement with ICRP deposition fractions (1.04% relative difference).
Stochastic analysis of ¹³¹I intake revealed that the published ICRP reference committed effective dose coefficient (CEDC) slightly exceeds the 75th percentile of computed samples.
Particle transport rates scaling factor and alveolar deposition efficiency were identified as the most impactful parameters in the sensitivity analysis.

Conclusions:

The study demonstrates the utility of a stochastic approach for modeling inhaled particulate metabolism, offering a more comprehensive understanding of radiation dose variability.
This enhanced modeling capability is crucial for improving radiation consequence management, informing medical countermeasures, and refining dose reconstruction for epidemiological studies.