VP1-12 Machine learning-based prediction model for magnetic hyperthermia
Creators: Donald Chu
Asahi Tomitaka
Publisher: name: IEEE
Year: 2025

Magnetic hyperthermia has great potential as a non-invasive cancer treatment. However, the limited success in clinical translation stems from a lack of systematic nanoparticle optimization. To bridge this gap, machine learning models that predict the heating efficiency of magnetic nanoparticles (MNPs) have been developed in a few studies [1][2]. Even though deep learning that uses artificial neural networks (ANNs) outperform traditional machine learning models in many cases, the ANNs trained on experimental data have not been studied for magnetic hyperthermia. In this study, the ANN models with different architectures were explored to predict the heating efficiencies of MNPs. The dataset containing physical and magnetic properties of MNPs and external magnetic field conditions as input features and specific absorption rate (SAR) representing the heat generation efficiency of MNPs as a target variable was collected through the literature search and the existing database [2]. As the performance of the ANN model largely depends on the architecture, the optimal architecture was explored by varying the number of neurons and hidden layers. The R2 values of the ANN models improved from 0.790 to 0.883 by increasing the number of neurons from 10 to 500 in a hidden layer. Figures 1 and 2 show the scatter plots of actual vs predicted SARs of the ANN models with 2 hidden layers of 500 neurons and 4 hidden layers of 500 neurons, respectively. The diagonal line represents the ideal fit where predicted SAR is always equal to actual SAR. The model performance was further improved by adding the second hidden layer of 500 neurons. However, a deeper architecture having more than 3 hidden layers reduced their prediction performances with higher errors. These results indicate that a shallow ANN is suitable to capture the patterns in this dataset and the optimized ANN model could be a great tool to design optimal MNPs.References: [1] M. Coïsson, G. Barrera, F. Celegato, et al., APL Materials, 2022, 10, 081108. [2] P. Kim, N. Serov, A. Falchevskaya, et al., Small 2023, 19, 2303522.
Magnetic hyperthermia has great potential as a non-invasive cancer treatment. However, the limited success in clinical translation stems from a lack of systematic nanoparticle optimization. To bridge this gap, machine learning models that predict the heating efficiency of magnetic nanoparticles (MNPs) have been developed in a few studies [1][2]. Even though deep learning that uses artificial neural networks (ANNs) outperform traditional machine learning models in many cases, the ANNs trained on experimental data have not been studied for magnetic hyperthermia. In this study, the ANN models with different architectures were explored to predict the heating efficiencies of MNPs. The dataset containing physical and magnetic properties of MNPs and external magnetic field conditions as input features and specific absorption rate (SAR) representing the heat generation efficiency of MNPs as a target variable was collected through the literature search and the existing database [2]. As the performance of the ANN model largely depends on the architecture, the optimal architecture was explored by varying the number of neurons and hidden layers. The R2 values of the ANN models improved from 0.790 to 0.883 by increasing the number of neurons from 10 to 500 in a hidden layer. Figures 1 and 2 show the scatter plots of actual vs predicted SARs of the ANN models with 2 hidden layers of 500 neurons and 4 hidden layers of 500 neurons, respectively. The diagonal line represents the ideal fit where predicted SAR is always equal to actual SAR. The model performance was further improved by adding the second hidden layer of 500 neurons. However, a deeper architecture having more than 3 hidden layers reduced their prediction performances with higher errors. These results indicate that a shallow ANN is suitable to capture the patterns in this dataset and the optimized ANN model could be a great tool to design optimal MNPs.References: [1] M. Coïsson, G. Barrera, F. Celegato, et al., APL Materials, 2022, 10, 081108. [2] P. Kim, N. Serov, A. Falchevskaya, et al., Small 2023, 19, 2303522.