Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs

Abstract

Abstract 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.