Effective Dose Estimation in Computed Tomography by Machine Learning
Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for...
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MDPI AG
2025-01-01
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| Series: | Tomography |
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| author | Matteo Ferrante Paolo De Marco Osvaldo Rampado Laura Gianusso Daniela Origgi |
| author_facet | Matteo Ferrante Paolo De Marco Osvaldo Rampado Laura Gianusso Daniela Origgi |
| author_sort | Matteo Ferrante |
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| description | Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning. Methods: In total, 69,037 CT acquisitions were collected with the dose-tracking software (DTS) available at our institution. E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R<sup>2</sup> were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions. Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R<sup>2</sup>: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%. Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach. |
| format | Article |
| id | doaj-art-1b9cc328d27649ada880c598f26faae5 |
| institution | Kabale University |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Tomography |
| spelling | doaj-art-1b9cc328d27649ada880c598f26faae52025-01-24T13:50:51ZengMDPI AGTomography2379-13812379-139X2025-01-01111210.3390/tomography11010002Effective Dose Estimation in Computed Tomography by Machine LearningMatteo Ferrante0Paolo De Marco1Osvaldo Rampado2Laura Gianusso3Daniela Origgi4Medical Physics Unit, IEO, European Institute of Oncology IRCCS, 20141 Milan, ItalyMedical Physics Unit, IEO, European Institute of Oncology IRCCS, 20141 Milan, ItalyMedical Physics Unit, A.O.U. Città della Salute e della Scienza di Torino, 10126 Turin, ItalyMedical Physics Unit, A.O.U. Città della Salute e della Scienza di Torino, 10126 Turin, ItalyMedical Physics Unit, IEO, European Institute of Oncology IRCCS, 20141 Milan, ItalyBackground: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning. Methods: In total, 69,037 CT acquisitions were collected with the dose-tracking software (DTS) available at our institution. E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R<sup>2</sup> were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions. Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R<sup>2</sup>: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%. Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.https://www.mdpi.com/2379-139X/11/1/2artificial intelligence (AI)patient radiation protectiondose tracking |
| spellingShingle | Matteo Ferrante Paolo De Marco Osvaldo Rampado Laura Gianusso Daniela Origgi Effective Dose Estimation in Computed Tomography by Machine Learning Tomography artificial intelligence (AI) patient radiation protection dose tracking |
| title | Effective Dose Estimation in Computed Tomography by Machine Learning |
| title_full | Effective Dose Estimation in Computed Tomography by Machine Learning |
| title_fullStr | Effective Dose Estimation in Computed Tomography by Machine Learning |
| title_full_unstemmed | Effective Dose Estimation in Computed Tomography by Machine Learning |
| title_short | Effective Dose Estimation in Computed Tomography by Machine Learning |
| title_sort | effective dose estimation in computed tomography by machine learning |
| topic | artificial intelligence (AI) patient radiation protection dose tracking |
| url | https://www.mdpi.com/2379-139X/11/1/2 |
| work_keys_str_mv | AT matteoferrante effectivedoseestimationincomputedtomographybymachinelearning AT paolodemarco effectivedoseestimationincomputedtomographybymachinelearning AT osvaldorampado effectivedoseestimationincomputedtomographybymachinelearning AT lauragianusso effectivedoseestimationincomputedtomographybymachinelearning AT danielaoriggi effectivedoseestimationincomputedtomographybymachinelearning |