Key result
Machine learning models reduced the simulation runtime of MitraClip intervention outcomes from an average of 6 hours to less than 1 second, achieving a mean absolute percentage error of 0.115 for mitral regurgitation using XGBoost.
Why the study?
Finite element modeling can simulate outcomes of different MitraClip scenarios but requires hours to complete, making it unsuitable for real-time clinical use.
Do machine learning algorithms accurately predict mitral regurgitation and leaflet stress in finite element models of MitraClip intervention?
Do machine learning algorithms accurately predict mitral regurgitation and leaflet stress in finite element models of MitraClip intervention?
Machine learning models can predict the outcomes of MitraClip implantation in near real-time, potentially enabling rapid, patient-specific procedural planning.
Answers are grounded in this paper and cite their sources.
Enables rapid MitraClip outcome simulation; hypothesis-generating for patient-specific planning pending clinical validation.
Introduction: Severe mitral regurgitation (MR) is a mitral valve disease that can lead to lifethreatening complications. MitraClip (MC) therapy is a percutaneous solution for patients who cannot tolerate surgical solutions. In MC therapy, a clip is implanted in the heart to reduce MR. To achieve optimal MC therapy, the cardiologist needs to foresee the outcomes of different scenarios for MC implantation, including the location of the MC. Although finite element (FE) modeling can simulate the outcomes of different MC scenarios, it is not suitable for clinical usage because it requires several hours to complete. Methods: In this paper, we used machine learning (ML) to predict the outcomes of MC therapy in less than 1 s. Two ML algorithms were used: XGBoost, which is a decision tree model, and a feed-forward deep learning (DL) model. The MC location, the geometrical attributes of the models and baseline stress and MR were the features of the ML models, and the predictions were performed for MR and maximum von Mises stress in the leaflets. The parameters of the ML models were determined to achieve the minimum errors obtained by applying the ML models on the validation set. Results: The results for the test set (not used during training) showed relative agreement between ML predictions and ground truth FE predictions. The accuracy of the XGBoost models were better than DL models. Mean absolute percentage error (MAPE) for the XGBoost predictions were 0.115 and 0.231, and the MAPE for DL predictions were 0.154 and 0.310, for MR and stress, respectively. Discussion: The ML models reduced the FE runtime from 6 hours (on average) to less than 1 s. The accuracy of ML models can be increased by increasing the dataset size. The results of this study have important implications for improving the outcomes of MC therapy by providing information about the outcomes of MC implantation in real-time.
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Dabiri et al. (2023) studied Severe mitral regurgitation (n=1,267). Machine learning models (XGBoost and Deep Learning) vs. Finite element (FE) modeling was evaluated on Mean absolute percentage error (MAPE) for predicting mitral regurgitation (MR) and maximum leaflet von Mises stress. Machine learning models reduced the simulation runtime of MitraClip intervention outcomes from an average of 6 hours to less than 1 second, achieving a mean absolute percentage error of 0.115 for mitral regurgitation using XGBoost.
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