A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter

In the present age, marked by data-driven advancements in various fields, the importance of machine learning (ML) holds a prominent position. The ability of ML algorithms to resolve complex patterns and extract insights from large datasets has solidified its transformative potential in various scientific domains. This paper introduces an innovative application of ML techniques in the domain of radiation dosimetry. Specifically, it shows the applicability of ML in estimating the radiation dose received by occupational workers. This estimation is expressed in terms of personal dose equivalent, and it involves the utilization of thermoluminescence signals emitted by CaSO4:Dy-based personnel monitoring badges. To estimate personal dose equivalent, three-stage algorithm driven by ML models is proposed. This algorithm systematically identifies the photon energy ranges, calculates the average photon energy, and determines personal dose equivalent. By implementing this approach to the conventional three-element dosimeter, the study overcomes existing limitations and enhances accuracy in dose estimation. The algorithm demonstrates 97.8% classification accuracy in discerning photon energy ranges and achieves a coefficient of determination of 0.988 for estimating average photon energy. Importantly, it also reduces the coefficient of variation of relative deviations by up to 6% for estimated personal dose equivalent, compared to existing algorithms. The study improves accuracy and establishes a new methodology for evaluating radiation exposure to occupational workers using conventional thermoluminescent dosimeter badge.