This study introduces a methodology for applying artificial intelligence techniques to build an internal dosimetry prediction toolkit for nuclear medical pediatric applications. Based on distinct anatomical characteristics, Monte Carlo simulations were run as a benchmark to accurately predict absorbed doses per organ in pediatric patients.
The study generated a simulated dosimetry database for 28 pediatric phantoms and five radiopharmaceuticals by using a population of computational pediatric models along with GATE Monte Carlo simulations. This dataset was then used to build machine learning regression models by applying ensemble learning and hyperparameter optimization methods.
The resulting toolkit can predict specific absorbed dose rates (SADRs) in 30 organs for five different radiopharmaceuticals in pediatric patients with high precision (<2.7 % uncertainty, >90 % accuracy), delivering quick results (<2 seconds). This approach can be extended to other medical dosimetry applications in diverse patient populations.
References and further reading:
Eleftheriadis, V., et al. (2023). A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques. Physics in Medicine & Biology, 68(8), p.084004. https://doi.org/10.1088/1361-6560/acc4a5.
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