Machine learning to predict environmental dose rates from a radionuclide therapy service: a proof of concept study

Abstract

The Ionising Radiation Regulations 2017 requires prior risk assessment calculations and regular environmental monitoring of radiation doses. However, the accuracy of prior risk assessments is limited by assumptions and monitoring only provides retrospective evaluation. This is particularly challenging in nuclear medicine for areas surrounding radionuclide therapy patient bathroom wastewater pipework. Machine learning (ML) is a technique that could be applied to patient booking records to predict environmental radiation dose rates in these areas to aid prospective risk assessment calculations, which this proof-of-concept work investigates. 540 days of a dosimeters historical daily average dose rate measurements and the corresponding period of department therapy booking records were used to train six different ML models. Predicted versus measured daily average dose rates for the following 60 days were analysed to assess and compare model performance. A wide range in prediction errors was observed across models. The gradient boosting regressor produced the best accuracy (root mean squared error = 1.10µSv.hr-1, mean absolute error = 0.87µSv.hr-1, mean absolute percentage error = 35% and maximum error = 3.26µSv.hr-1) and goodness of fit (R2= 0.411). Methods to improve model performance and other scenarios where this approach could prove more accurate were identified. This work demonstrates that ML can predict temporal fluctuations in environmental radiation dose rates in the areas surrounding radionuclide therapy wastewater pipework and indicates that it has the potential to play a role in improving legislative compliance, the accuracy of radiation safety and use of staff time and resources.

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ORCID for James Clark Ross: orcid.org/0000-0001-8626-2041

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