Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Dabiri, Yaghoub; Jiang, Yao; Mahadevan, Vaikom S.; Gruber, Daniel A.; Arnaout, Rima; Gentzsch, Wolfgang; Guccione, Julius M.; Kassab, Ghassan S.

doi:10.3389/fcvm.2021.759675

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…The details of the database generation have been described elsewhere ( Dabiri et al, 2021 ). In brief, our database was created from MV geometries obtained from different resources, including echocardiography patients’ data, morphological data from the literature ( Krawczyk-Ożóg et al, 2017 ), and principal component analysis (PCA).…”

Section: Methodsmentioning

confidence: 99%

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Machine learning used for simulation of MitraClip intervention: A proof-of-concept study

Dabiri

Mahadevan²,

Guccione³

et al. 2023

Front. Genet.

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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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Section: Methodsmentioning

confidence: 99%

“…To address the limitations of FE simulation of MC therapy, we applied ML-based methods to obtain the outcomes of MR in real-time. For this, we used a dataset of FE models that we created for different MV geometries and MC locations ( Dabiri et al, 2021 ). There were six possible locations for the MC.…”

Section: Introductionmentioning

confidence: 99%

Machine learning used for simulation of MitraClip intervention: A proof-of-concept study

Dabiri

Mahadevan²,

Guccione³

et al. 2023

Front. Genet.

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“…One utilization of both finite element and fluid structure interaction models leverages an established atlas of shapes and complex scenarios to make clinical decisions on MitraClip™ implantation ( 24 ). To compose the atlas, mitral valve geometrical data was acquired from three different sources.…”

Section: Foundational Work On Computational Modeling For Tmvrmentioning

confidence: 99%

“…FIGURE 1Finite element model of left ventricle and mitral valve apparatus (left) and mitral valve leaflets with six locations for possible MitraClip TM placement (right)(24).…”

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Looking towards the future: patient-specific computational modeling to optimize outcomes for transcatheter mitral valve repair

Wong

Wisneski

Sandhu

et al. 2023

Front. Cardiovasc. Med.

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Severe mitral valve regurgitation (MR) is a heart valve disease that progresses to end-stage congestive heart failure and death if left untreated. Surgical repair or replacement of the mitral valve (MV) remains the gold standard for treatment of severe MR, with repair techniques aiming to restore the native geometry of the MV. However, patients with extensive co-morbidities may be ineligible for surgical intervention. With the emergence of transcatheter MV repair (TMVR) treatment paradigms for MR will evolve. The longer-term outcomes of TMVR and its effectiveness compared to surgical repair remain unknown given the differing patient eligibility for either treatment at this time. Advances in computational modeling will elucidate answers to these questions, employing techniques such as finite element method and fluid structure interactions. Use of clinical imaging will permit patient-specific MV models to be created with high accuracy and replicate MV pathophysiology. It is anticipated that TMVR technology will gradually expand to treat lower-risk patient groups, thus pre-procedural computational modeling will play a crucial role guiding clinicians towards the optimal intervention. Additionally, concerted efforts to create MV models will establish atlases of pathologies and biomechanics profiles which could delineate which patient populations would best benefit from specific surgical vs. TMVR options. In this review, we describe recent literature on MV computational modeling, its relevance to MV repair techniques, and future directions for translational application of computational modeling for treatment of MR.

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Patient-Specific Quantitative In-Vivo Assessment of Human Mitral Valve Leaflet Strain Before and After MitraClip Repair

Simonian,

Liu,

Vakamudi

et al. 2023

Cardiovasc Eng Tech

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Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Cited by 12 publications

References 29 publications

Machine learning used for simulation of MitraClip intervention: A proof-of-concept study

Machine learning used for simulation of MitraClip intervention: A proof-of-concept study

Looking towards the future: patient-specific computational modeling to optimize outcomes for transcatheter mitral valve repair

Patient-Specific Quantitative In-Vivo Assessment of Human Mitral Valve Leaflet Strain Before and After MitraClip Repair

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