## 4 Discussion  
Several studies have investigated factors influencing CT radiation dose. [PERSON] et al. [20] conducted a study to determine patient, vendor and institutional factors that influence CT radiation dose. They employed linear regression and modified Poisson regression to identify the parameters contributing to dose variability. The authors found that CT dose varies within and across medical centers. Moreover, they found that patient size, institutional-specific protocols and multiphase scanning were the most important predictors of dose followed by manufacturer and iterative reconstruction. The relationship between CTDI\({}_{\text{vol}}\) and patient parameters has been explored in the literature. [PERSON] et al. [21] proposed a study to find associations between different CT dose estimates, namely CTDI\({}_{\text{vol}}\), DLP and SSDE, and patient age, BMI, scan length and technical  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{4}{*}{ \begin{tabular}{c} **CTDI\({}_{\text{vol}}\)** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: DLP** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: ED** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: SSDE** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: SSDE** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: SSDE** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **X1: SSDE** \\ \end{tabular}  } \\ \cline{3-4}  & & R\({}^{2}\) & 0.85 & 0.26 & 0.37 & 0.19 & 0.19 & 0.68 \\  & RMSE & 294.94 & 5.47 & 2.44 & 300.25 & 295.55 & 5.17 \\  & MAE & 268.43 & 4.83 & 1.94 & 273.25 & 268.32 & 4.94 \\  & MAPE & 97.06 & 193.59 & 25.89 & 98.84 & 96.80 & 63.28 \\  & Eq. & \(\hat{y}=38.50\,\mathrm{X_{1}}-\hat{y}=0.21\,\mathrm{X_{1}}+\hat{y}=0.49\, \mathrm{X_{1}}+\hat{y}=45.05\,\mathrm{X_{1}}+\hat{y}=22.91\,\mathrm{X_{1}}+ \hat{y}=1.61\,\mathrm{X_{1}}+3.15\) \\  & 2.14 & 1.30 & 4.08 & 144.52 & 96.15 & \(\hat{y}=1.61\,\mathrm{X_{1}}+3.15\) \\  & \(r\) & 0.92 & 0.51 & 0.61 & 0.44 & 0.44 & 0.82 \\  & \(p\)-value & \(4.86\times 10^{-26}\) & \(2.17\times 10^{-5}\) & \(1.55\times 10^{-7}\) & \(3.52\times 10^{-4}\) & \(3.66\times 10^{-4}\) & \(2.77\times 10^{-16}\) \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} **CTDI\({}_{\text{vol}}\)** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} R\({}^{2}\) \\ RMSE \\ MAE \\ MAPE \\ Eq. \\ \end{tabular}  } & 0.74 & - & - & - & - & - & - \\  & & RMSE & 593.64 & - & - & - & - & - \\  & MAE & 572.96 & - & - & - & - & - \\  & MAPE & 94.13 & - & - & - & - & - \\  & Eq. & \(\hat{y}=0.04\,\mathrm{X_{1}}+\hat{y}=0.49\,\mathrm{X_{1}}+\hat{y}=45.05\, \mathrm{X_{1}}+\hat{y}=45.05\,\mathrm{X_{1}}+\hat{y}=22.91\,\mathrm{X_{1}}+ \hat{y}=1.61\,\mathrm{X_{1}}+3.15\) \\  & \(r\) & 0.86 & - & - & - & - & - \\  & \(p\)-value & \(1.26\times 10^{-24}\) & - & - &parameters such as tube current, tube voltage, pitch, noise level and level of iterative reconstruction. It was found all dose estimates in all imaging protocols were affected by tube current. [PERSON] et al. [22] utilized machine learning techniques to predict CT radiation dose and discovered a relationship between the diagnostic reference level (DRL) and BMI, with increasing CTDIvol as BMI rises. As demonstrated in Section 3, it can be noted that our results are in agreement with the results found in [22]. Additionally, [PERSON] et al. [23] conducted linear regression analysis on a dataset of 500 adult CT examinations and found a linear increase in CTDIvol with patient size, while SSDE remained independent of size. With respect to DLP, [PERSON] et al. [24] investigated the relationship between radiation exposure and compositions of the abdominal cavity. They found that abdominal adipose tissue is the strongest contributor to DLP in abdominopelvic CT, followed by muscle volume. In [25], multivariate logistic regression analysis was performed to determine the factors affecting the radiation dose index (DLP) in pulmonary angiogram CT examinations.