Sudden Cardiac Death Substrate Imaged by Magnetic Resonance Imaging

Wu, Kathérine C.

doi:10.1161/circimaging.116.005461

Cited by 59 publications

(34 citation statements)

References 89 publications

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“…Given immediate capacity for segmented datasets to be fused to live fluoroscopic images, a paradigm of image‐guided personalized therapeutics is emerging. Further, the work described in this sentinel study provides new opportunities to pursue rapid, standardized scar quantification for risk stratification in patients with ischemic cardiomyopathy, a field strongly justified by expanding publications from 2D LGE data …”

Section: Discussionmentioning

confidence: 99%

“…Replacement myocardial fibrosis or “scar” is a recognized substrate for the development of malignant ventricular arrhythmias and is a dominant cause of sudden cardiac death in the population . The most common mechanism of scar formation in the heart is from myocardial infarction (MI), an irreversible death of muscle cells related to blockage of a coronary artery.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

2019

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Purpose: Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. Methods: Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. Results: Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% AE 2.6% and 88.13% AE 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. Conclusions: Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

2019

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“…39 Prevention of SCD among TOF patients remains a major challenge, especially in reliably identifying the high-risk patients, and perhaps over-reliance on depressed LV function for primary prevention with ICD. 49 The combination of moderate to severe LV systolic dysfunction (EF < 20%) and QRS >180 ms had a strong positive and negative predictive value, 66% and 93%, respectively for predicting SCD. 41 A multicenter observational cohort study of 870 patients identified RV hypertrophy, LV or RV systolic dysfunction (EF <44%) and atrial tachyarrhythmias were predictors of SCD or sustained ventricular tachycardia in young adult patients with repaired TOF.…”

Section: Case 1 Conclusionmentioning

confidence: 90%

Tetralogy of Fallot: Case-Based Update for the Treatment of Adult Congenital Patients

Englert¹,

Gupta²,

Joury³

et al. 2019

Current Problems in Cardiology

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“…To accelerate the integration of ML into clinical care, an emphasis on the personalization of these tools for the end user is crucial. Our work is motivated by the well-known clinical challenge of measuring an individual's risk of sudden cardiac death (SCD), a leading cause of death with inherently complex pathophysiology that lends itself to novel approaches [ 17 - 22 ]. Although we focus here on SCD as an illustrative example, the methods we present are applicable more broadly to improving clinical translation of ML, regardless of the specific ML algorithm or clinical application.…”

Section: Introductionmentioning

confidence: 99%

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Wongvibulsin

Zeger

2020

JMIR Med Inform

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Background Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms. Objective The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML. Methods To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death. Results With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients. Conclusions Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.

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Sudden Cardiac Death Substrate Imaged by Magnetic Resonance Imaging

Cited by 59 publications

References 89 publications

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

Tetralogy of Fallot: Case-Based Update for the Treatment of Adult Congenital Patients

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

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