Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction

Puyol‐Antón, Esther; Chen, Chen; Clough, James R.; Ruijsink, Bram; Sidhu, Baldeep S.; Gould, Justin; Porter, Bradley; Elliott, Mark K.; Mehta, Vishal; Rueckert, Daniel; Rinaldi, Christopher A.; King, Andrew P.

doi:10.1007/978-3-030-59710-8_28

Cited by 27 publications

(35 citation statements)

References 15 publications

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“…Another approach is to embed known clinical concepts into deep learning models. In previous study, we have proposed a method that ensures clinical concepts (in this case ejection fraction or septal flash) are encoded into the latent space of a variational autoencoder, a dimensionality reduction algorithm frequently used for classification [33]. This approach allows to solve the classification process while simultaneously interrogating the importance of known clinical features in the decision process.…”

Section: Interpretabilitymentioning

confidence: 99%

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

et al. 2022

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Purpose of Review As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. Recent Findings and Summary There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. Graphical Abstract AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines

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Section: Interpretabilitymentioning

confidence: 99%

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

et al. 2022

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“…Puyol-Antón et al ( 127 ) offered in the first of its kind, an interpretable approach to a DL model for the prediction of CRT response. This framework was based around a DL-based generative model known as a variational autoencoder (VAE) which encodes the segmented biventricular data into a low dimensional latent space, followed by a primary task classifier of predicting those who would respond to CRT utilizing pre-treatment CMR images.…”

Section: Clinical Risk Prediction and The Role For Aimentioning

confidence: 99%

The Role of AI in Characterizing the DCM Phenotype

Asher

Puyol‐Antón

Rizvi

et al. 2021

Front. Cardiovasc. Med.

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Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.

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“…That is why more focus is recently put on designing self-explainable models [4,7] to make the decision process directly visible. Many interpretable solutions are based on the attention [32,53,60,[62][63][64] or exploit the activation space [18,42], e.g. with adversarial autoencoder.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Image Classification with Differentiable Prototypes Assignment

Rymarczyk¹,

Struski²,

Górszczak³

et al. 2021

Preprint

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We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes. The training is more straightforward than in the existing methods because it does not require the pruning stage. It is obtained by introducing a fully differentiable assignment of prototypes to particular classes. Moreover, we introduce a novel focal similarity function to focus the model on the rare foreground features. We show that Pro-toPool obtains state-of-the-art accuracy on the CUB-200-2011 and the Stanford Cars datasets, substantially reducing the number of prototypes. We provide a theoretical analysis of the method and a user study to show that our prototypes are more distinctive than those obtained with competitive methods. We made the code available.

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Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction

Cited by 27 publications

References 15 publications

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

The Role of AI in Characterizing the DCM Phenotype

Interpretable Image Classification with Differentiable Prototypes Assignment

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