Prediction of paravalvular leak post transcatheter aortic valve replacement using a convolutional neural network

Wang, Zih Huei; Lahoti, Geet; Wang, Kan; Liu, Shizhen; Zhang, Chuck; Wang, Ben; Wu, Chien‐Wei; Vannan, Mani A.; Qian, Zhen

doi:10.1109/isbi.2018.8363760

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Table 4 compares these studies in terms of the utilized datasets, targeted problems, deployed DL models, and achieved results. Wang et al [103] proposed a compact 3-layer CNN classification model to predict paravalvular leak (PVL) post TAVR, which is a major complication of the TAVR procedure. The proposed model predicted PVL from post-operative CT data, achieving sensitivity and specificity values of 76.91% and 86.88%, respectively.…”

Section: Outcome Assessment Of Tavr Procedures Using Machine/deep Lea...mentioning

confidence: 99%

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Tahir,

Mutlu,

Bensaali

et al. 2023

JCM

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Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid–solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

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Section: Outcome Assessment Of Tavr Procedures Using Machine/deep Lea...mentioning

confidence: 99%

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Tahir,

Mutlu,

Bensaali

et al. 2023

JCM

View full text Add to dashboard Cite

show abstract

“…For the prediction of surgical outcomes, the rationality of that is not clear, because a surgery involves a complex and dynamic interaction between the human anatomy and the surgical device, and the visual cues extracted from the medical images may not be sufficient for such a prediction. In one of our recent work, the predictive performance for transcatheter aortic valve replacement (TAVR) outcome using transfer learning is inferior to a CNN learnt from scratch [6]. This urges us to explore other possibilities.…”

Section: Introductionmentioning

confidence: 99%

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Xie

Wang

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable.

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The Evolving Role of Artificial Intelligence in Cardiac Image Analysis

Thériault-Lauzier

Avram

Dey

et al. 2022

Canadian Journal of Cardiology

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Prediction of paravalvular leak post transcatheter aortic valve replacement using a convolutional neural network

Cited by 4 publications

References 12 publications

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

The Evolving Role of Artificial Intelligence in Cardiac Image Analysis

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