Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study.

Abstract

Objectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.

Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances.

Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses.

Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.