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1. AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography

Author

Puyol-Antón, Esther, Ruijsink, Bram, Sidhu, Baldeep S., Gould, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Gu, Haotian, Xochicale, Miguel, Gomez, Alberto, Rinaldi, Christopher A., Cowie, Martin, Chowienczyk, Phil, Razavi, Reza, and King, Andrew P.

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer description of cardiac function can be obtained by estimating functional biomarkers from the complete cardiac cycle. In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle. These biomarkers will allow clinicians to obtain a much richer picture of the heart in health and disease. The AI model is based on the 'nn-Unet' framework and was trained and tested using four different databases. Results show excellent agreement between manual and automated analysis and showcase the potential of the advanced systolic and diastolic biomarkers for patient stratification. Finally, for a subset of 50 cases, we perform a correlation analysis between clinical biomarkers derived from echocardiography and CMR and we show excellent agreement between the two modalities.

Published
2022

3. Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

Author

Dawood, Tareen, Chen, Chen, Andlauer, Robin, Sidhu, Baldeep S., Ruijsink, Bram, Gould, Justin, Porter, Bradley, Elliott, Mark, Mehta, Vishal, Rinaldi, C. Aldo, Puyol-Antón, Esther, Razavi, Reza, and King, Andrew P.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large sets of data but they often lack comprehensive interpretability methods, preventing clinical trust in predictive outcomes. Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. However, relatively little attention has been paid to how to include such requirements into the training of the model. In this paper we: (i) quantify the data (aleatoric) and model (epistemic) uncertainty of a DL model for Cardiac Resynchronisation Therapy response prediction from cardiac magnetic resonance images, and (ii) propose and perform a preliminary investigation of an uncertainty-aware loss function that can be used to retrain an existing DL image-based classification model to encourage confidence in correct predictions and reduce confidence in incorrect predictions. Our initial results are promising, showing a significant increase in the (epistemic) confidence of true positive predictions, with some evidence of a reduction in false negative confidence., Comment: STACOM 2021 Workshop

Published
2021

4. A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction

Author

Puyol-Antón, Esther, Sidhu, Baldeep S., Gould, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Rinaldi, Christopher A., and King, Andrew P.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.

Published
2021

5. A Framework for Auditing Data Center Energy Usage and Mitigating Environmental Footprint

Author

Gould, Justin

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase. This increase in data availability and demand for analytics will put a strain on data centers and compute clusters-with implications for both energy costs and emissions. As the world battles a climate crisis, it is prudent for organizations with data centers to have a framework for combating increasing energy costs and emissions to meet demand for analytics work. In this paper, I present a generalized framework for organizations to audit data centers energy efficiency to understand the resources required to operate a given data center and effective steps organizations can take to improve data center efficiency and lower the environmental impact., Comment: 9 pages, 2 figures

Published
2021

6. Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction

Author

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

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models - most models are learnt either from scratch or using transfer learning from a different domain. In this paper we address both of these issues. We propose a novel DL framework for image-based classification based on a variational autoencoder (VAE). The framework allows prediction of the output of interest from the latent space of the autoencoder, as well as visualisation (in the image domain) of the effects of crossing the decision boundary, thus enhancing the interpretability of the classifier. Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge. The framework can predict outputs as well as explanations for these outputs, and also raises the possibility of discovering new biomarkers that are separate (or disentangled) from the existing knowledge. We demonstrate our framework on the problem of predicting response of patients with cardiomyopathy to cardiac resynchronization therapy (CRT) from cine cardiac magnetic resonance images. The sensitivity and specificity of the proposed model on the task of CRT response prediction are 88.43% and 84.39% respectively, and we showcase the potential of our model in enhancing understanding of the factors contributing to CRT response., Comment: MICCAI 2020 conference

Published
2020
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7. Guided implantation of a leadless left ventricular endocardial electrode and acoustic transmitter using computed tomography anatomy, dynamic perfusion and mechanics, and predicted activation pattern

Author

Sidhu, Baldeep S., Lee, Angela W.C., Gould, Justin, Porter, Bradley, Sieniewicz, Benjamin, Elliott, Mark K., Mehta, Vishal S., Wijesuriya, Nadeev, Amadou, Abdoul A., Plank, Gernot, Haberland, Ulrike, Rajani, Ronak, Rinaldi, Christopher A., and Niederer, Steven A.

Published
2023
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9. Improving complex cardiac implantable electronic device therapy outcomes

Author

Gould, Justin, Rinaldi, Christopher Aldo, and Razavi, Reza

Subjects
616.1
Abstract

Cardiac resynchronisation therapy (CRT) devices and implantable cardioverter defibrillators (ICDs) are frequently implanted in patients with heart failure and those at risk of ventricular arrhythmia (VA). Approximately 30-50% of patients undergoing CRT implantation fail to derive any improvement, depending on heart failure aetiology and the metric used to evaluate response. Furthermore, appropriate ICD therapy only occurs in one third of patients implanted with an ICD indicating better risk stratification of VA is needed. This thesis therefore aims to explore novel ways to improve complex cardiac implantable electronic device therapy outcomes. Initially, this thesis explores the feasibility and potential benefit of using real-time cardiac CT image overlay guidance and multisite left ventricular (LV) pacing as two distinct approaches to improve CRT response rates through optimal LV lead delivery. Real-time CT image overlay appears safe and feasible with significant improvements in echocardiographic volumetric response outcomes at 6-months follow-up compared to baseline. However, whilst multisite LV pacing appears feasible and safe, no evidence was found to support its use in improving CRT response in patients with left bundle branch block and intermediate QRS prolongation of 120-150 ms. Finally, this thesis investigates the role of scar heterogeneity, quantified by mean entropy using cardiac magnetic resonance texture analysis (CMR-TA), as a potential metric for ICD risk stratification. For the first time, mean entropy, calculated using CMRTA, was identified as an independent predictor of appropriate ICD therapy in patients with mixed cardiomyopathy and ischaemic cardiomyopathy-only, suggesting a potential role in predicting VAs and risk stratifying patients for ICD implantation. Furthermore, lower scar heterogeneity, quantified by mean entropy using cardiac MRI texture analysis, was found to be associated with successful ATP whereas higher scar heterogeneity was associated with more aggressive VAs unresponsive to ATP requiring shock therapy.

Published
2020

10. Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

Author

Dawood, Tareen, Chen, Chen, Andlauer, Robin, Sidhu, Baldeep S., Ruijsink, Bram, Gould, Justin, Porter, Bradley, Elliott, Mark, Mehta, Vishal, Rinaldi, C. Aldo, Puyol-Antón, Esther, Razavi, Reza, King, Andrew P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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13. Machine learning–derived major adverse event prediction of patients undergoing transvenous lead extraction: Using the ESC EHRA EORP European lead extraction ConTRolled ELECTRa registry

Author

Mehta, Vishal S., O’Brien, Hugh, Elliott, Mark K., Wijesuriya, Nadeev, Auricchio, Angelo, Ayis, Salma, Blomstrom-Lundqvist, Carina, Bongiorni, Maria Grazia, Butter, Christian, Deharo, Jean-Claude, Gould, Justin, Kennergren, Charles, Kuck, Karl-Heinz, Kutarski, Andrzej, Leclercq, Christophe, Maggioni, Aldo P., Sidhu, Baldeep S., Wong, Tom, Niederer, Steven, and Rinaldi, Christopher A.

Published
2022
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20. Comprehensive use of cardiac computed tomography to guide left ventricular lead placement in cardiac resynchronization therapy

Author

Behar, Jonathan M, Rajani, Ronak, Pourmorteza, Amir, Preston, Rebecca, Razeghi, Orod, Niederer, Steve, Adhya, Shaumik, Claridge, Simon, Jackson, Tom, Sieniewicz, Ben, Gould, Justin, Carr-White, Gerry, Razavi, Reza, McVeigh, Elliot, and Rinaldi, Christopher Aldo

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Heart Disease, Bioengineering, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Evaluation of treatments and therapeutic interventions, 6.3 Medical devices, Aged, Cardiac Resynchronization Therapy, Electrocardiography, Female, Follow-Up Studies, Heart Failure, Heart Ventricles, Humans, Male, Predictive Value of Tests, Reproducibility of Results, Time Factors, Tomography, X-Ray Computed, Ventricular Function, Left, Ventricular Remodeling, Cardiac computed tomography, Cardiac resynchronization therapy, CT guided intervention, Dyssynchrony, Myocardial fibrosis, Biomedical Engineering, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundOptimal lead positioning is an important determinant of cardiac resynchronization therapy (CRT) response.ObjectiveThe purpose of this study was to evaluate cardiac computed tomography (CT) selection of the optimal epicardial vein for left ventricular (LV) lead placement by targeting regions of late mechanical activation and avoiding myocardial scar.MethodsEighteen patients undergoing CRT upgrade with existing pacing systems underwent preimplant electrocardiogram-gated cardiac CT to assess wall thickness, hypoperfusion, late mechanical activation, and regions of myocardial scar by the derivation of the stretch quantifier for endocardial engraved zones (SQUEEZ) algorithm. Cardiac venous anatomy was mapped to individualized American Heart Association (AHA) bull's-eye plots to identify the optimal venous target and compared with acute hemodynamic response (AHR) in each coronary venous target using an LV pressure wire.ResultsFifteen data sets were evaluable. CT-SQUEEZ-derived targets produced a similar mean AHR compared with the best achievable AHR (20.4% ± 13.7% vs 24.9% ± 11.1%; P = .36). SQUEEZ-derived guidance produced a positive AHR in 92% of target segments, and pacing in a CT-SQUEEZ target vein produced a greater clinical response rate vs nontarget segments (90% vs 60%).ConclusionPreprocedural CT-SQUEEZ-derived target selection may be a valuable tool to predict the optimal venous site for LV lead placement in patients undergoing CRT upgrade.

Published
2017

21. AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography

Author

Puyol-Antón, Esther, primary, Ruijsink, Bram, additional, Sidhu, Baldeep S., additional, Gould, Justin, additional, Porter, Bradley, additional, Elliott, Mark K., additional, Mehta, Vishal, additional, Gu, Haotian, additional, Rinaldi, Christopher A., additional, cowie, Martin, additional, Chowienczyk, Phil, additional, Razavi, Reza, additional, and King, Andrew P., additional

Published
2022
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22. His-bundle and left bundle pacing with optimized atrioventricular delay achieve superior electrical synchrony over endocardial and epicardial pacing in left bundle branch block patients

Author

Strocchi, Marina, Lee, Angela W.C., Neic, Aurel, Bouyssier, Julien, Gillette, Karli, Plank, Gernot, Elliott, Mark K., Gould, Justin, Behar, Jonathan M., Sidhu, Baldeep, Mehta, Vishal, Bishop, Martin J., Vigmond, Edward J., Rinaldi, Christopher A., and Niederer, Steven A.

Published
2020
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25. Predicting Post-Infarct Ventricular Tachycardia by Integrating Cardiac MRI and Advanced Computational Reentrant Pathway Analysis

Author

Bhagirath, Pranav, primary, Campos, Fernando O., additional, Zaidi, Hassan A., additional, Chen, Zhong, additional, Elliott, Mark, additional, Gould, Justin, additional, Kemme, Michiel J.B., additional, Wilde, Arthur A.M., additional, Götte, Marco J.W., additional, Postema, Pieter, additional, Prassl, Anton J., additional, Neic, Aurel, additional, Plank, Gernot, additional, Rinaldi, Christopher A., additional, and Bishop, Martin J., additional

Published
2024
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42. Laparoscopic Partial Nephrectomy

Author

Kenney, Patrick, Gould, Justin, Tsai, Sheaumei, Canes, David, Dasgupta, Prokar, editor, Fitzpatrick, John, editor, Kirby, Roger, editor, and Gill, Inderbir S., editor

Published
2010
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43. Convergent ablation for persistent atrial fibrillation: outcomes from a single-centre real-world experience

Author

Mannakkara, Nilanka N, primary, Porter, Bradley, additional, Child, Nicholas, additional, Sidhu, Baldeep S, additional, Mehta, Vishal S, additional, Elliott, Mark K, additional, Gould, Justin, additional, Ahmed, Shahada, additional, Razavi, Reza, additional, Rinaldi, Christopher A, additional, Blauth, Christopher, additional, and Gill, Jaswinder S, additional

Published
2022
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