Computer methods and programs in biomedicine · 2026
Prediction of high-frequency irreversible electroporation (H-FIRE) threshold via deep learning combined with genetic algorithm.
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Tian Yang, Wu Xinghui, Yang Chun
Аннотация
BACKGROUND AND OBJECTIVE: High-frequency irreversible electroporation (H-FIRE) has shown promise in various medical applications. However, the complexity of the H-FIRE process makes it difficult to accurately predict the electric-field (E-field) lethal threshold, which is crucial for safe and effective treatment planning.
METHODS: Here, we propose a prediction method integrated with deep learning (DL) and genetic algorithm (GA). Model interpretability is assessed using local interpretable model-agnostic explanation (LIME), and predictive performance is compared with traditional finite element simulation.
RESULTS: The initial DL framework achieves a mean absolute error (MAE) of 0.02 on the training set and 0.03 on the test set, though some predictions deviate considerably from the true values. After GA optimization, the training MAE decreases to 0.013 (a 35% reduction) and the test MAE decreases to 0.023 (a 23% reduction). The maximal training error also shows a significant decrease in the lethal threshold from 1.4 to 0.3 kV·cm-1, with only 8% of test errors exceeding 0.2 kV·cm-1 (a 47% reduction). In addition, the optimized DL-GA model achieves a remarkable decrease in real MAE (0.06 kV·cm-1) compared to previous reports (0.511 kV·cm-1).
CONCLUSION: The proposed DL-GA framework enables rapid and accurate prediction of H-FIRE lethal thresholds, offering a potential tool for future clinical practice.
METHODS: Here, we propose a prediction method integrated with deep learning (DL) and genetic algorithm (GA). Model interpretability is assessed using local interpretable model-agnostic explanation (LIME), and predictive performance is compared with traditional finite element simulation.
RESULTS: The initial DL framework achieves a mean absolute error (MAE) of 0.02 on the training set and 0.03 on the test set, though some predictions deviate considerably from the true values. After GA optimization, the training MAE decreases to 0.013 (a 35% reduction) and the test MAE decreases to 0.023 (a 23% reduction). The maximal training error also shows a significant decrease in the lethal threshold from 1.4 to 0.3 kV·cm-1, with only 8% of test errors exceeding 0.2 kV·cm-1 (a 47% reduction). In addition, the optimized DL-GA model achieves a remarkable decrease in real MAE (0.06 kV·cm-1) compared to previous reports (0.511 kV·cm-1).
CONCLUSION: The proposed DL-GA framework enables rapid and accurate prediction of H-FIRE lethal thresholds, offering a potential tool for future clinical practice.