scholarly journals Deep Learning to Predict Cardiac Magnetic Resonance–Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Shaan Khurshid ◽  
Samuel Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Left Ventricular Mass ◽  
Left Ventricular ◽  
Uk Biobank ◽  
C Statistic ◽  
Lv Mass ◽  
Test Sets ◽  
The Uk

Background: Classical methods for detecting left ventricular (LV) hypertrophy (LVH) using 12-lead ECGs are insensitive. Deep learning models using ECG to infer cardiac magnetic resonance (CMR)-derived LV mass may improve LVH detection. Methods: Within 32 239 individuals of the UK Biobank prospective cohort who underwent CMR and 12-lead ECG, we trained a convolutional neural network to predict CMR-derived LV mass using 12-lead ECGs (left ventricular mass-artificial intelligence [LVM-AI]). In independent test sets (UK Biobank [n=4903] and Mass General Brigham [MGB, n=1371]), we assessed correlation between LVM-AI predicted and CMR-derived LV mass and compared LVH discrimination using LVM-AI versus traditional ECG-based rules (ie, Sokolow-Lyon, Cornell, lead aVL rule, or any ECG rule). In the UK Biobank and an ambulatory MGB cohort (MGB outcomes, n=28 612), we assessed associations between LVM-AI predicted LVH and incident cardiovascular outcomes using age- and sex-adjusted Cox regression. Results: LVM-AI predicted LV mass correlated with CMR-derived LV mass in both test sets, although correlation was greater in the UK Biobank (r=0.79) versus MGB (r=0.60, P<0.001 for both). When compared with any ECG rule, LVM-AI demonstrated similar LVH discrimination in the UK Biobank (LVM-AI c-statistic 0.653 [95% CI, 0.608 -0.698] versus any ECG rule c-statistic 0.618 [95% CI, 0.574 -0.663], P=0.11) and superior discrimination in MGB (0.621; 95% CI, 0.592 -0.649 versus 0.588; 95% CI, 0.564 -0.611, P=0.02). LVM-AI-predicted LVH was associated with incident atrial fibrillation, myocardial infarction, heart failure, and ventricular arrhythmias. Conclusions: Deep learning-inferred LV mass estimates from 12-lead ECGs correlate with CMR-derived LV mass, associate with incident cardiovascular disease, and may improve LVH discrimination compared to traditional ECG rules.

scholarly journals Deep Learning to Estimate Cardiac Magnetic Resonance-Derived Left Ventricular Mass

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Samuel Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Open Source ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Learning Model ◽  
Left Ventricular ◽  
Lv Mass ◽  
Deep Learning Model

ABSTRACTBackgroundCardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (e.g., inlineVF), but their accuracy and availability may be limited.ObjectiveTo develop an open-source deep learning model to estimate CMR-derived LV mass.MethodsWithin participants of the UK Biobank prospective cohort undergoing CMR, we trained two convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5,065 individuals), while the second (ML4Hseg) performed LV segmentation informed by inlineVF contours. We compared ML4Hreg, ML4Hseg, and inlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex.ResultsWe generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r=0.864, 95% CI 0.847-0.880; MAE 10.41g, 95% CI 9.82-10.99) than ML4Hreg (r=0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, p=0.01) and inlineVF (r=0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, p<0.01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.53, 95% CI 3.16-6.33).ConclusionsML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

scholarly journals Clinical and Genetic Associations of Deep Learning-Derived Cardiac Magnetic Resonance-Based Left Ventricular Mass

2022 ◽  
Author(s):  
Shaan Khurshid ◽  
Julieta Lazarte ◽  
James Pirruccello ◽  
Lu-Chen Weng ◽  
Seung Hoan Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Genome Wide Association Study ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Genome Wide ◽  
A Genome

Increased left ventricular (LV) mass (LVM) and LV hypertrophy (LVH) are risk markers for adverse cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance (CMR) is the gold standard for LVM estimation, but is challenging to obtain at scale, which has limited the power of prior genetic analyses. In the current study, we performed a genome-wide association study (GWAS) of CMR-derived LVM indexed to body surface area (LVMI) estimated using a deep learning algorithm within nearly 50,000 participants from the UK Biobank. We identified 12 independent associations (1 known at TTN and 11 novel) meeting genome-wide significance, implicating several candidate genes previously associated with cardiac contractility and cardiomyopathy. Greater CMR-derived LVMI was associated with higher risk of incident dilated (hazard ratio [HR] 2.58 per 1-SD increase, 95% CI 2.10-3.17) and hypertrophic (HR 2.62, 95% CI 2.09-3.30) cardiomyopathies. A polygenic risk score (PRS) for LVMI was also associated with incident hypertrophic cardiomyopathy within a separate set of UK Biobank participants (HR] 1.12, 95% CI 1.01-1.12) and among individuals in an external Mass General Brigham dataset (HR 1.18, 95% CI 1.01-1.37). In summary, using CMR-derived LVM available at scale, we have identified 12 common variants associated with LVMI (11 novel) and demonstrated that both CMR-derived and genetically determined LVMI are associated with risk of incident cardiomyopathy.

scholarly journals Deep learning to estimate cardiac magnetic resonance–derived left ventricular mass

2021 ◽  
Vol 2 (2) ◽  
pp. 109-117
Author(s):  
Shaan Khurshid ◽  
Samuel Freesun Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Left Ventricular Mass ◽  
Left Ventricular ◽  
Ventricular Mass

scholarly journals Prognostic significance of cardiac magnetic resonance-based markers in patients with hypertrophic cardiomyopathy

2021 ◽  
Author(s):  
Zsofia Dohy ◽  
Liliana Szabo ◽  
Attila Toth ◽  
Csilla Czimbalmos ◽  
Rebeka Horvath ◽  
...  
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Myocardial Fibrosis ◽  
Ventricular Arrhythmias ◽  
Prognostic Significance ◽  
Strain Analysis ◽  
Left Ventricular ◽  
Icd Therapy ◽  
Lv Mass

AbstractThe prognosis of patients with hypertrophic cardiomyopathy (HCM) varies greatly. Cardiac magnetic resonance (CMR) is the gold standard method for assessing left ventricular (LV) mass and volumes. Myocardial fibrosis can be noninvasively detected using CMR. Moreover, feature-tracking (FT) strain analysis provides information about LV deformation. We aimed to investigate the prognostic significance of standard CMR parameters, myocardial fibrosis, and LV strain parameters in HCM patients. We investigated 187 HCM patients who underwent CMR with late gadolinium enhancement and were followed up. LV mass (LVM) was evaluated with the exclusion and inclusion of the trabeculae and papillary muscles (TPM). Global LV strain parameters and mechanical dispersion (MD) were calculated. Myocardial fibrosis was quantified. The combined endpoint of our study was all-cause mortality, heart transplantation, malignant ventricular arrhythmias and appropriate implantable cardioverter defibrillator (ICD) therapy. The arrhythmia endpoint was malignant ventricular arrhythmias and appropriate ICD therapy. The LVM index (LVMi) was an independent CMR predictor of the combined endpoint independent of the quantification method (p < 0.01). The univariate predictors of the combined endpoint were LVMi, global longitudinal (GLS) and radial strain and longitudinal MD (MDL). The univariate predictors of arrhythmia events included LVMi and myocardial fibrosis. More pronounced LV hypertrophy was associated with impaired GLS and increased MDL. More extensive myocardial fibrosis correlated with impaired GLS (p < 0.001). LVMi was an independent CMR predictor of major events, and myocardial fibrosis predicted arrhythmia events in HCM patients. FT strain analysis provided additional information for risk stratification in HCM patients.

Relation of B-type natriuretic peptide to left ventricular wall stress as assessed by cardiac magnetic resonance imaging in patients with dilated cardiomyopathy

2007 ◽  
Vol 85 (8) ◽  
pp. 790-799 ◽  
Author(s):  
P. Alter ◽  
H. Rupp ◽  
M.B. Rominger ◽  
A. Vollrath ◽  
F. Czerny ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Wall Stress ◽  
Left Ventricular ◽  
Ventricular Wall ◽  
Resonance Imaging ◽  
Sphere Model ◽  
Lv Mass ◽  
Systolic Wall Stress

Ventricular loading conditions are crucial determinants of cardiac function and prognosis in heart failure. B-type natriuretic peptide (BNP) is mainly stored in the ventricular myocardium and is released in response to an increased ventricular filling pressure. We examined, therefore, the hypothesis that BNP serum concentrations are related to ventricular wall stress. Cardiac magnetic resonance imaging (MRI) was used to assess left ventricular (LV) mass and cardiac function of 29 patients with dilated cardiomyopathy and 5 controls. Left ventricular wall stress was calculated by using a thick-walled sphere model, and BNP was assessed by immunoassay. LV mass (r = 0.73, p < 0.001) and both LV end-diastolic (r = 0.54, p = 0.001) and end-systolic wall stress (r = 0.66, p < 0.001) were positively correlated with end-diastolic volume. LV end-systolic wall stress was negatively related to LV ejection fraction (EF), whereas end-diastolic wall stress was not related to LVEF. BNP concentration correlated positively with LV end-diastolic wall stress (r = 0.50, p = 0.002). Analysis of variance revealed LV end-diastolic wall stress as the only independent hemodynamic parameter influencing BNP (p < 0.001). The present approach using a thick-walled sphere model permits determination of mechanical wall stress in a clinical routine setting using standard cardiac MRI protocols. A correlation of BNP concentration with calculated LV stress was observed in vivo. Measurement of BNP seems to be sufficient to assess cardiac loading conditions. Other relations of BNP with various hemodynamic parameters (e.g., EF) appear to be secondary. Since an increased wall stress is associated with cardiac dilatation, early diagnosis and treatment could potentially prevent worsening of the outcome.

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