Related Experiment Video
Updated: Jul 21, 2026
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Artificial Neural Network-based Model for Predicting Cardiologists' Over-apron Dose in CATHLABs
Reza Fardid1,2, Fatemeh Farah1, Hossein Parsaei3
1Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Insights
An artificial neural network (ANN) model accurately predicts cardiologist radiation exposure in catheterization labs using dose area product (DAP). This advanced model enhances safety protocols for interventional cardiology professionals.
Area of Science:
- Medical Physics
- Radiological Protection
- Artificial Intelligence in Medicine
Background:
- Cardiologists in catheterization labs face significant occupational radiation exposure.
- Overlooking dosimeter use due to high-stress tasks necessitates predictive models for radiation doses.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) model for predicting over-apron radiation dose in cardiologists.
- To assess the efficacy of ANN compared to traditional models for radiation dose prediction.
Main Methods:
- An ANN model was trained using data from Monte Carlo simulations with varying X-ray spectra and tube orientations.
- Input features included dose area product (DAP), energy spectrum, and tube angulation.
- A multilayer perceptron neural network was utilized for prediction.
Main Results:
- The ANN model achieved high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 µSv.
- The ANN model significantly outperformed a linear regression model (R-value=0.48, RMSE=18.15 µSv).
Conclusions:
- ANN models offer a powerful and accurate tool for predicting occupational radiation doses in clinical settings.
- The developed model can enhance safety protocols and enable real-time exposure assessment for cardiologists.
- Future work should focus on integrating these models into real-time monitoring systems.
Aim:
The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists' radiation exposure.
Materials And Methods:
This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist's dose.
Results:
The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 µSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 µSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses.
Conclusion:
This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
