Tanyildizi-Kokkulunk Handan
Radiotherapy Program, 187458 Vocational School of Health Sciences, Altinbas University , Istanbul, Türkiye.
Biomed Tech (Berl). 2025 Apr 8;70(4):385-391. doi: 10.1515/bmt-2024-0620. Print 2025 Aug 26.
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.
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.
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.
The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.
在本研究中，旨在运用机器学习（ML）算法，在考虑最常用的CT协议的情况下，准确预测体模的辐射剂量。
利用基于云的软件计算不同CT协议的有效剂量。为模拟一系列不同体重的成年患者，使用了八组基于全身网格的计算体模。将头部、颈部以及胸部-腹部-骨盆CT扫描特征相结合，为每个体模创建一个包含33行的数据集中，总共792行。在机器学习阶段，使用了线性回归（LR）、随机森林（RF）和支持向量回归（SVR）。采用平均绝对误差、均方误差和准确率来评估性能。
女性体模接受的剂量比男性高（7.8%）。此外，正常体重体模的平均受量比超重体模多11%，超重体模与肥胖I型体模相比，肥胖I型体模与肥胖II型体模相比也是如此。在机器学习算法中，线性回归在预测CT剂量时显示出0错误率和100%的准确率。
在用于CT诱导剂量的机器学习估计的方法中，线性回归被证明是最佳方法。
