BackgroundAdults born very preterm have increased cardiac mass and reduced function. We investigated whether a hypertrophic phenomenon occurs in later preterm infants and when this occurs during ...early development.MethodsCardiac ultrasound was performed on 392 infants (33% preterm at mean gestation 34±2 weeks). Scans were performed during fetal development in 137, at birth and 3 months of postnatal age in 200, and during both fetal and postnatal development in 55. Cardiac morphology and function was quantified and computational models created to identify geometric changes.ResultsAt birth, preterm offspring had reduced cardiac mass and volume relative to body size with a more globular heart. By 3 months, ventricular shape had normalized but both left and right ventricular mass relative to body size were significantly higher than expected for postmenstrual age (left 57.8±41.9 vs. 27.3±29.4%, P<0.001; right 39.3±38.1 vs. 16.6±40.8, P=0.002). Greater changes were associated with lower gestational age at birth (left P<0.001; right P=0.001).ConclusionPreterm offspring, including those born in late gestation, have a disproportionate increase in ventricular mass from birth up to 3 months of postnatal age. These differences were not present before birth. Early postnatal development may provide a window for interventions relevant to long-term cardiovascular health.
Background Pregnancy complications such as preterm birth and fetal growth restriction are associated with altered prenatal and postnatal cardiac development. We studied whether there were changes ...related specifically to pregnancy hypertension. Methods and Results Left and right ventricular volumes, mass, and function were assessed at birth and 3 months of age by echocardiography in 134 term-born infants. Fifty-four had been born to mothers who had normotensive pregnancy and 80 had a diagnosis of preeclampsia or pregnancy-induced hypertension. Differences between groups were interpreted, taking into account severity of pregnancy disorder, sex, body size, and blood pressure. Left and right ventricular mass indexed to body surface area (LVMI and RVMI) were similar in both groups at birth (LVMI 20.9±3.7 versus 20.6±4.0 g/m
,
=0.64, RVMI 17.5±3.7 versus 18.1±4.7 g/m
,
=0.57). However, right ventricular end diastolic volume index was significantly smaller in those born to hypertensive pregnancy (16.8±5.3 versus 12.7±4.7 mL/m
,
=0.001), persisting at 3 months of age (16.4±3.2 versus 14.4±4.8 mL/m
,
=0.04). By 3 months of age these infants also had significantly greater LVMI and RVMI (LVMI 24.9±4.6 versus 26.8±4.9 g/m
,
=0.04; RVMI 17.1±4.2 versus 21.1±3.9 g/m
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<0.001). Differences in RVMI and right ventricular end diastolic volume index at 3 months, but not left ventricular measures, correlated with severity of the hypertensive disorder. No differences in systolic or diastolic function were evident. Conclusions Infants born at term to a hypertensive pregnancy have evidence of both prenatal and postnatal differences in cardiac development, with right ventricular changes proportional to the severity of the pregnancy disorder. Whether differences persist long term as well as their underlying cause and relationship to increased cardiovascular risk requires further study.
In this paper we consider automated myocardial wall motion assessment by quantifying a cardiac bull's eye map derived from principal strain analysis. The objective is to learn a classification model ...that can classify between normal and abnormal wall motions. A traditional hand-crafted feature approach based on pixel intensities is compared with a deep learning framework, where a Convolutional Neural Network (CNN) automatically learns features. Experiments on a 3D Dobutamine Stress Echo (DSE) dataset with normal and abnormal wall motions shows that both hand-crafted approaches yield comparable accuracy: Random Forests (72.1%), Support Vector Machines (70.5%), and CNN at a slightly higher accuracy (75.0%) and a lower training computational cost.
Abstract only Background: An artificial intelligence (AI) model to diagnose heart failure (HF) with preserved ejection fraction (HFpEF) based on deep learning of echocardiographic (TTE) images could ...improve under-recognition of disease. However, the diagnostic accuracy in high-risk patients, compared to existing clinical prediction models, remains uncertain. Methods: In this retrospective case-control study, we identified patients who received routine TTE at Beth Israel Deaconess Medical Center, Harvard Medical School. Cases were defined by presence of ICD-10 codes for HF (I50.X), LVEF≥50%, and grade II or III diastolic dysfunction. Controls were defined by absence of HF within 1 year of the index TTE. The discriminatory performance of an AI HFpEF model (EchoGo Heart Failure, Ultromics Ltd.) was compared to the clinical H2FPEF score. Results: Both cases (n=213, 75±12 years, 53% female, 29.4±7.1 kg/m2) and controls (n=226, 75±13 years, 54% female, 27.0±5.6 kg/m2) were high-risk for HFpEF, with high rates of hypertension, atrial fibrillation, diabetes, coronary artery, kidney, and pulmonary disease. The H2FPEF score demonstrated high sensitivity (mean 95% CI; 97.3% 94.3, 100%) and modest specificity (38.1% 25.9, 55.9%). The AI HFpEF model had similar sensitivity (88.8% 84.0, 93.4%) and slightly improved specificity (55.6% 47.7, 64.0%). In the 265 patients (60.4%) with non-diagnostic H2FPEF scores, the AI HFpEF model successfully classified 144 patients as probable HFpEF ( Figure ). The H2FPEF score identified 31 (54.5%) whereas the AI HFpEF model identified 45 (78.9%) HFpEF patients who were hospitalized within 1 year of the TTE. Conclusions: An AI model to detect HFpEF improves upon identification of HFpEF amongst high-risk individuals, thus potentially improving the capacity to identify patients who might benefit from treatment.
The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation.
Coronary ...artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests.
An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. study. How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study.
Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93.
Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.
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Hypertensive pregnancy is associated with increased maternal cardiovascular risk in later life. A range of cardiovascular adaptations after pregnancy have been reported to partly explain this risk. ...We used multimodality imaging to identify whether, by midlife, any pregnancy-associated phenotypes were still identifiable and to what extent they could be explained by blood pressure. Participants were identified by review of hospital maternity records 5 to 10 years after pregnancy and invited to a single visit for detailed cardiovascular imaging phenotyping. One hundred seventy-three women (age, 42±5 years, 70 after normotensive and 103 after hypertensive pregnancy) underwent magnetic resonance imaging of the heart and aorta, echocardiography, and vascular assessment, including capillaroscopy. Women with a history of hypertensive pregnancy had a distinct cardiac geometry with higher left ventricular mass index (49.9±7.1 versus 46.0±6.5 g/m; P=0.001) and ejection fraction (65.6±5.4% versus 63.7±4.3%; P=0.03) but lower global longitudinal strain (−18.31±4.46% versus −19.94±3.59%; P=0.02). Left atrial volume index was also increased (40.4±9.2 versus 37.3±7.3 mL/m; P=0.03) and E:A reduced (1.34±0.35 versus 1.52±0.45; P=0.003). Aortic compliance (0.240±0.053 versus 0.258±0.063; P=0.046) and functional capillary density (105.4±23.0 versus 115.2±20.9 capillaries/mm; P=0.01) were reduced. Only differences in functional capillary density, left ventricular mass, and atrial volume indices remained after adjustment for blood pressure (P<0.01, P=0.01, and P=0.04, respectively). Differences in cardiac structure and geometry, as well as microvascular rarefaction, are evident in midlife after a hypertensive pregnancy, independent of blood pressure. To what extent these phenotypic patterns contribute to cardiovascular disease progression or provide additional measures to improve risk stratification requires further study.
Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide, affecting 2.3 million people in the UK. Whilst CAD can be diagnosed through a variety of imaging modalities, ...stress echocardiography is the most commonly used technique, used in over 50% of hospitals in the UK. However, the current clinical standard for stress echo is very subjective and is only 81 % sensitive and 82% specific. Therefore, there is interest in the development of robust imaging biomarkers which can reliably and reproducibly provide a predictive index for prognostically significant CAD. The aim of this thesis is to develop and test methods that improve clinical diagnosis of coronary artery disease in stress echocardiograms using machine learning for 1) image contouring 2) producing features from the contours and 3) predicting coronary artery disease. A framework for auto contouring of stress echocardiograms was developed, initially using two and four chamber cardiac views for both contrast enhanced and noncontrast echocardiograms, which was later expanded to short-axis (SAX) views. Images were obtained from the prospective multisite EVAREST trial imaging database. The contouring technology was based on a U-net convolutional neural network (CNN) and was trained using contours of the endocardial border created manually, comprised of 5692 and 2182 frames for the contrast and non-contrast application, respectively. The autocontouring achieved a dice coefficient score of 93% for contrast images and 92% for non-contrast images. Following autocontouring, features were generated from the 2-chamber (AP2) and 4- chamber (AP4) contour output. The feature generation methodology was validated by performing a retrospective study in 146 patients to compare the automated outputs of ejection fraction (EF) and global longitudinal strain (GLS) against equivalent measures generated using standard TomTec analysis software. EF demonstrated a root mean square error (RMSE) of 7.36. GLS demonstrated a RMSE of 2.76. Following validations of contouring and feature calculation these features and additional novel features were inputted into a machine learning classifier to produce a binary high/low risk prediction for CAD. The training dataset consisted of 633 patients. The final trained model was an ensemble. The area under a Receiver Operator Characteristic Curve (AUROC) of 0.93 was obtained on the training data using a nested cross validation. The model was then validated in an entirely independent dataset and a similar AUROC of 0.93 was obtained. These results demonstrate the utility of artificial intelligence for automated echocardiography analysis and, for the first time, an ability to predict clinical outcome directly from echo images.
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