Contrast-Enhanced Echocardiographic Measurement of Left Ventricular Wall Thickness in Hypertrophic Cardiomyopathy: Comparison with Standard Echocardiography and Cardiac Magnetic Resonance

Urbano-Moral, José Ángel; Gonzalez-Gonzalez, Ana M.; Maldonado, Giuliana; Gutierrez-Garcia-Moreno, Laura; Vivancos-Delgado, Ricardo; Mora-Martín, Manuel de; Rodríguez-Palomares, José F.; Evangelista-Masip, Arturo

doi:10.1016/j.echo.2020.04.009

Cited by 16 publications

(11 citation statements)

References 24 publications

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“…The Mayo and Toronto scoring systems include a limited number of parameters determined by echocardiography-e.g., maximal LV wall thickness, septal morphological subtypes. However, these parameters have been known to have large intra-and interobserver variability (11,30). It is a time-and cost-intensive process to train physicians until they can accurately measure the wall thickness and classify the septal morphology (31).…”

Section: Advantages Of Deep Learning-based Approach Over the Conventional Prediction Methodsmentioning

confidence: 99%

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Morita

Kusunose

Haga

et al. 2021

Front. Cardiovasc. Med.

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Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.

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Section: Advantages Of Deep Learning-based Approach Over the Conventional Prediction Methodsmentioning

confidence: 99%

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Morita

Kusunose

Haga

et al. 2021

Front. Cardiovasc. Med.

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“…Wall measurements obtained with UEA may be more reproducible than those obtained without and are more closely aligned to measurements obtained by CMR. 30 Additionally, UEA may facilitate identification of myocardial crypts, apical aneurysms, and ventricular thrombi, and can facilitate myocardial target localization for alcohol septal ablation. Systematic identification and exclu-sion of RV structures (including trabeculation, moderator band, and crista supraventricularis) is advised when measuring the interventricular septum (IVS).…”

Section: A Assessment Of Left Ventricular Hypertrophymentioning

confidence: 99%

Recommendations for Multimodality Cardiovascular Imaging of Patients with Hypertrophic Cardiomyopathy: An Update from the American Society of Echocardiography, in Collaboration with the American Society of Nuclear Cardiology, the Society for Cardiovascular Magnetic Resonance, and the Society of Cardiovascular Computed Tomography

Nagueh

Phelan

Abraham

et al. 2022

Journal of the American Society of Echocardiography

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“…HCM is characterized by unexplained left ventricular asymmetric hypertrophy and the absence of cardiac or systemic triggers. MCE has been proposed as a helpful diagnosis tool in questioned HCM and the precise determination of left ventricular wall thickness by MCE is of great significance in the prognostic assessment of patients with HCM ( 80 ). Recently, CMD, representing a predisposing factor for myocardial ischemia, has also been recognized as one of the most important pathophysiological features.…”

Section: Assessment Of Cmd By Mce In Different Disease Categoriesmentioning

confidence: 99%

Assessment and Treatment for Coronary Microvascular Dysfunction by Contrast Enhanced Ultrasound

Zhan

Zhong²,

Wu³

2022

Front. Cardiovasc. Med.

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With growing evidence in clinical practice, the understanding of coronary syndromes has gradually evolved out of focusing on the well-established link between stenosis of epicardial coronary artery and myocardial ischemia to the structural and functional abnormalities at the level of coronary microcirculation, known as coronary microvascular dysfunction (CMD). CMD encompasses several pathophysiological mechanisms of coronary microcirculation and is considered as an important cause of myocardial ischemia in patients with angina symptoms without obstructive coronary artery disease (CAD). As a result of growing knowledge of the understanding of CMD assessed by multiple non-invasive modalities, CMD has also been found to be involved in other cardiovascular diseases, including primary cardiomyopathies as well as heart failure with preserved ejection fraction (HFpEF). In the past 2 decades, almost all the imaging modalities have been used to non-invasively quantify myocardial blood flow (MBF) and promote a better understanding of CMD. Myocardial contrast echocardiography (MCE) is a breakthrough as a non-invasive technique, which enables assessment of myocardial perfusion and quantification of MBF, exhibiting promising diagnostic performances that were comparable to other non-invasive techniques. With unique advantages over other non-invasive techniques, MCE has gradually developed into a novel modality for assessment of the coronary microvasculature, which may provide novel insights into the pathophysiological role of CMD in different clinical conditions. Moreover, the sonothrombolysis and the application of artificial intelligence (AI) will offer the opportunity to extend the use of contrast ultrasound theragnostics.

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Contrast-Enhanced Echocardiographic Measurement of Left Ventricular Wall Thickness in Hypertrophic Cardiomyopathy: Comparison with Standard Echocardiography and Cardiac Magnetic Resonance

Cited by 16 publications

References 24 publications

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy

Assessment and Treatment for Coronary Microvascular Dysfunction by Contrast Enhanced Ultrasound

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