Incidentally identified left ventricular apical aneurysm in a patient with Fabry disease.

Fabry disease is a rare X-linked lysosomal storage disorder caused by a deficiency in the lysosomal enzyme, galactosidase A, that can result in a progressive increase in the left ventricle (LV) wall thickness from glycosphingolipid deposition leading to myocardial fibrosis, conduction abnormalities, arrhythmias, and heart failure. We present a case of a patient with advanced Fabry cardiomyopathy, in whom a small LV apical aneurysm was incidentally discovered on abdominal imaging, which could have easily evaded detection on standard transthoracic echocardiography. The LV apex should be thoroughly interrogated in patients with Fabry cardiomyopathy, as the finding of LV aneurysm could have important management implications with respect to the prevention of stroke and sudden cardiac death.

Point-of-care ultrasound in the COVID-19 era: A scoping review.

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Multimodality imaging of a pulmonary artery sarcoma.

Pulmonary artery sarcoma is a rare malignant neoplasm. Here, we describe a patient with a pulmonary artery sarcoma, which was only subtly visible and therefore not fully appreciated on initial transthoracic echocardiogram. Characterization of the tumor was aided by the use of multimodality imaging that included computed tomography, magnetic resonance imaging, and positron emission tomography. Familiarity with its appearance on multiple imaging modalities including echocardiography is important to ensure timely diagnosis, although the optimal treatment strategy is still unknown, and the prognosis remains poor.

Best method for right atrial volume assessment by two-dimensional echocardiography: validation with magnetic resonance imaging.

AIM: Echocardiographic methods for estimating right atrial (RA) volume have not been standardized. Our aim was to evaluate two-dimensional (2D) echocardiographic methods of RA volume assessment, using RA volume by magnetic resonance imaging (MRI) as the reference. METHODS AND RESULTS: Right atrial volume was assessed in 51 patients (mean age 63 ± 14 years, 33 female) who underwent comprehensive 2D echocardiography and cardiac MRI for clinically indicated reasons. Echocardiographic RA volume methods included (1) biplane area length, using four-chamber view twice (biplane 4C-4C); (2) biplane area length, using four-chamber and subcostal views (biplane 4C-subcostal); and (3) single plane Simpson's method of disks (Simpson's). Echocardiographic RA volumes as well as linear RA major and minor dimensions were compared to RA volume by MRI using correlation and Bland-Altman methods, and evaluated for inter-observer reproducibility and accuracy in discriminating RA enlargement. All echocardiography volumetric methods performed well compared to MRI, with Pearson's correlation of 0.98 and concordance correlation ≥0.91 for each. For bias and limits of agreement, biplane 4C-4C (bias -4.81 mL/m(2) , limits of agreement ±9.8 mL/m(2) ) and Simpson's (bias -5.15 mL/m(2) , limits of agreement ±10.1 mL/m(2) ) outperformed biplane 4C-subcostal (bias -8.36 mL/m(2) , limits of agreement ±12.5 mL/m(2) ). Accuracy for discriminating RA enlargement was higher for all volumetric methods than for linear measurements. Inter-observer variability was satisfactory across all methods. CONCLUSIONS: Compared to MRI, biplane 4C-4C and single plane Simpson's are highly accurate and reproducible 2D echocardiography methods for estimating RA volume. Linear dimensions are inaccurate and should be abandoned.

Prevalence and spectrum of conditions associated with severe tricuspid regurgitation.

BACKGROUND: Data regarding the prevalence and spectrum of conditions associated with severe tricuspid regurgitation (TR) are limited to small cohorts. METHODS: We retrospectively identified all patients with severe native tricuspid valve regurgitation in a large echocardiogram database between January 2004 and December 2010. Patients were classified into 1 of 3 groups based on the echocardiogram results: (1) organic TR; (2) functional TR; and (3) idiopathic TR. RESULTS: Severe TR was identified in 768 (1.2%, 72 ± 16 years, 64.3% females) of 63, 472 consecutive patients referred for transthoracic echocardiography. The conditions associated with severe TR could be established in 91% of patients. The remaining 9% were classified as idiopathic severe TR with these patients being older (78 ± 10 years) and having a higher frequency of atrial fibrillation (63.8%) compared to patients with organic (65 ± 22 years; 31%) or functional severe TR (73 ± 16 years; 47.8%). Overall, organic severe TR was identified in 11.3% of all cases. Functional severe TR occurred in 79.7% of the overall cohort and was related to pulmonary hypertension and/or left-sided heart disease. CONCLUSION: Severe TR occurred with a prevalence of 1.2% in our patients referred for echocardiography and was more common in females. Functional severe TR was the most common etiology with only a minority of cases secondary to organic severe TR. Idiopathic severe TR was found in a small proportion of patients who were older and more likely to have atrial fibrillation.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

BACKGROUND: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.

Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.

BACKGROUND: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood. OBJECTIVES: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF. METHODS: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices. Video data were analyzed offline by an ML model for LVEF. We compared the ML model's performance with Level III echocardiographers' interpretation and echo reported LVEF. RESULTS: There were 138 participants scanned, yielding 1257 videos. The ML model generated LVEF predictions on 341 videos. We observed a good intraclass correlation (ICC) between the ML model's predictions and the reference standards (ICC = 0.77-0.84). When comparing LVEF estimates for randomized single POCUS videos, the ICC between the ML model and Level III echocardiographers' estimates was 0.772, and it was 0.778 for videos where quantitative LVEF was feasible. When the Level III echocardiographer reviewed all POCUS videos for a participant, the ICC improved to 0.794 and 0.843 when only accounting for studies that could be segmented. The ML model's LVEF estimates also correlated well with LVEF derived from formal echocardiogram reports (ICC = 0.798). CONCLUSION: Our results suggest that clinician-driven cardiac POCUS produces ML model LVEF estimates that correlate well with expert interpretation and echo reported LVEF.

Atrial remodeling in newly diagnosed drug-naive hypertensive subjects.

BACKGROUND: Systemic hypertension is a major risk factor for heart disease and stroke. Data regarding temporal relationship of left atrial (LA) remodeling to onset of hypertension are sparse. We aimed to quantitate LA structural and functional remodeling in newly diagnosed hypertensive patients. METHOD: We prospectively identified 380 patients with newly diagnosed systemic hypertension naive to drug therapy, and 380 age-matched control subjects without any history or evidence of hypertension. History or evidence of prior cardiovascular events, congenital or valvular heart disease, and renal dysfunction were exclusion criteria. Prevalence of LA enlargement, LA mechanical dysfunction expressed in total emptying fraction, left ventricular (LV) diastolic dysfunction, LV hypertrophy, and their interrelationships were assessed. RESULTS: Of the 380 newly diagnosed hypertensive patients, 285 (75%) had LA enlargement, 308 (81%) had LA mechanical dysfunction, and 19 (5%) had LVH. Diastolic dysfunction was present in 334 (88%) of the patients. Compared to the controls, the hypertensive group had larger maximal, minimal, and pre-A LA volumes (all P < 0.001). Total and active LA emptying fraction were significantly reduced (both P < 0.001). Total LA emptying fraction was strongly associated with systolic blood pressure [per 10 mmHg, HR 0.94 (0.89-0.98); P < 0.001], with stepwise decrease in LA emptying fraction of 6%, 10%, and 16% from the lowest (141-150 mmHg) to the top tertile of systolic blood pressure (>160 mmHg). CONCLUSION: In this drug-naive cohort with newly diagnosed hypertension, LA structural and functional remodeling, and LV diastolic dysfunction were common findings prior to initiation of drug treatment. LVH was uncommon. Impairment of LA mechanical function was evident even in the mildly hypertensive subgroup.

Quantification of pleural effusions by two-dimensional transthoracic echocardiography.

PURPOSE: There is lack of validated methods for quantifying the size of pleural effusion from standard transthoracic (TTE) windows. The purpose of this study is to determine whether pleural effusion (Peff) measured from routine two-dimensional (2D) TTE views correlate with chest radiograph (CXR). MATERIALS AND METHODS: We retrospectively identified all inpatients who underwent a TTE and CXR within 2 days in a large tertiary care center. Peff was measured on TTE from parasternal long axis (PLAX), apical four-chamber (A4C), and subcostal views and on CXR. Logistic regression models were used determine optimal cut points to predict moderate or greater Peff. RESULTS: In 200 patients (mean age 69.3 ± 14.3 years, 49.5% female), we found statistically significant associations between Peff size assessed by all TTE views and CXR, with weak to moderate correlation (PLAX length: 0.21 (95% CI [0.05, 0.35]); PLAX depth: 0.21 (95% CI [0.05, 0.35]); A4C left: 0.31 (95% CI [0.13, 0.46]); A4C right: 0.39 (95% CI [0.17, 0.57]); subcostal: 0.38 (95% CI [0.07, 0.61]). The best TTE thresholds for predicting moderate or greater left-sided Peff on CXR was PLAX length left > = 8.6 cm (sensitivity 78%, specificity 54%, PPV 26%, and NPV 92%). The best TTE thresholds for predicting moderate or greater right-sided Peff on CXR was A4C right > = 2.6 cm (sensitivity 87%, specificity 60%, PPV 37%, and NPV 94%). CONCLUSIONS: We identified statistically significant associations with Peff size measured on TTE and CXR. The predictive ability of TTE to identify moderate or large pleural effusion is limited.

Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality.

We sought to determine the cardiac ultrasound view of greatest quality using a machine learning (ML) approach on a cohort of transthoracic echocardiograms (TTE) with abnormal left ventricular (LV) systolic function. We utilize an ML model to determine the TTE view of highest quality when scanned by sonographers. A random sample of TTEs with reported LV dysfunction from 09/25/2017-01/15/2019 were downloaded from the regional database. Component video files were analyzed using ML models that jointly classified view and image quality. The model consisted of convolutional layers for extracting spatial features and Long Short-term Memory units to temporally aggregate the frame-wise spatial embeddings. We report the view-specific quality scores for each TTE. Pair-wise comparisons amongst views were performed with Wilcoxon signed-rank test. Of 1,145 TTEs analyzed by the ML model, 74.5% were from males and mean LV ejection fraction was 43.1 ± 9.9%. Maximum quality score was best for the apical 4 chamber (AP4) view (70.6 ± 13.9%, p<0.001 compared to all other views) and worst for the apical 2 chamber (AP2) view (60.4 ± 15.4%, p<0.001 for all views except parasternal short-axis view at mitral/papillary muscle level, PSAX M/PM). In TTEs scanned by professional sonographers, the view with greatest ML-derived quality was the AP4 view.

Cardiac Phenotyping of SARS-CoV-2 in British Columbia: A Prospective Echo Study With Strain Imaging.

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS.

A Novel Continuous Left Ventricular Diastolic Function Score Using Machine Learning.

BACKGROUND: Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function. OBJECTIVES: We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data. METHODS: Consecutive echo studies performed at a tertiary-care center between February 1, 2010, and March 31, 2016, were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which 1 or more parameters within the contemporary diastolic function assessment algorithm were not reported. Diastolic function was graded based on 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines, excluding indeterminate studies. Machine learning models were trained (support vector machine [SVM], decision tree [DT], XGBoost [XGB], and dense neural network [DNN]) to classify studies within the training set by diastolic dysfunction severity, blinded to the ASE/EACVI classification. The DNN model was retrained to generate a regression model (R-DNN) to predict a continuous LV diastolic function score. RESULTS: A total of 28,986 studies were included; 23,188 studies were used to train the models, and 5,798 studies were used for validation. The models were able to reclassify studies with high agreement to the ASE/EACVI algorithm (SVM, 83%; DT, 100%; XGB, 100%; DNN, 98%). The continuous diastolic function score corresponded well with ASE/EACVI guidelines, with scores of 1.00 ± 0.01 for studies with normal function and 0.74 ± 0.05, 0.51 ± 0.06, and 0.27 ± 0.11 for mild, moderate, and severe diastolic dysfunction, respectively (mean ± 1 SD). A score of <0.91 predicted abnormal diastolic function (area under the receiver operator curve = 0.99), while a score of <0.65 predicted elevated filling pressure (area under the receiver operator curve = 0.99). CONCLUSIONS: Machine learning can assimilate echocardiographic data and generate an automated continuous diastolic function score that corresponds well with current diastolic function grading recommendations.

Prevalence of left ventricular systolic dysfunction by single echocardiographic view: towards an evidence-based point of care cardiac ultrasound scanning protocol.

Limited views are often obtained in the setting of cardiac ultrasound, however, the likelihood of missing left ventricular (LV) dysfunction based on a single view is not known. We sought to determine the echo views that were least likely to miss LV systolic dysfunction in consecutive transthoracic echocardiograms (TTEs). Structured data from TTEs performed at 2 hospitals from September 25, 2017, to January 15, 2019, were screened. Studies of interest were those with reported LV dysfunction. Views evaluated were the parasternal long-axis (PLAX), parasternal-short axis at mitral (PSAX M), papillary muscle (PSAX PM), and apical (PSAX A) levels, apical 2 (AP2), apical 3 (AP3), and apical 4 (AP4) chamber views. The probability that a view contained at least 1 abnormal segment was determined and analyzed with McNemar's test for 21 adjusted pair-wise comparisons. There were 4102 TTE studies included for analysis. TTEs on males comprised 72.7% of studies with a mean LV ejection fraction of 42.8 ± 9.7%. The echo view with the greatest likelihood of encompassing an abnormal segment was the AP2 view with a prevalence of 93.4% (p < 0.001, compared to all other views). The PLAX view performed the worst with a prevalence of 82.5% (p < 0.015, compared to all other views). The best parasternal view for the detection of abnormality was the PSAX PM view at 90.4%. In conclusions, a single echo view will contain abnormal segments > 82% of the time in the setting of LV systolic dysfunction, with a prevalence of up to 93.4% in the apical windows.

Relationship between enlarged cardiac silhouette on chest X-ray and left ventricular size on transthoracic echocardiography.

The diagnostic accuracy of the cardiothoracic ratio on chest X-ray to detect left ventricular (LV) enlargement has not been well defined despite its traditional association with cardiomegaly. We aimed to determine whether the cardiothoracic ratio can accurately predict LV enlargement based on indexed linear measurements of the LV on transthoracic echocardiography (TTE). We included consecutive patients who had a TTE and a posteroanterior chest X-ray performed within 90 days of each other at a tertiary care center. LV size was determined by measuring the LV end-diastolic dimension (LVEDD) and LV end-diastolic dimension indexed (LVEDDI) to body surface area. The cardiothoracic ratio was calculated by dividing the maximum transverse diameter of the cardiac silhouette by the maximum transverse diameter of the right and left lung boundaries. 173 patients were included in the study (mean age 68 ± 15 years, 49.1% female). Mean cardiothoracic ratio was 0.56 ± 0.09, and the mean LVEDD and indexed LVEDDI were of 47 ± 8.6 mm and dimension of 27 ± 4.5 mm/m2 respectively. There was no significant correlation between the cardiothoracic ratio measured on chest X-ray and either the LVEDD or LVEDDI measured on TTE (r = 0.011, p = 0.879; r = 0.122, p = 0.111). The ability of the cardiothoracic ratio to predict LV enlargement (defined as LVEDDI > 30 mm/m2) was not statistically significant. The cardiothoracic ratio on chest X-ray is not a predictor of LV enlargement based on indexed linear measurements of the LV by TTE.

Impact of the updated diastolic function guidelines in the real world.

BACKGROUND: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. METHODS: We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. RESULTS: When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318-0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. CONCLUSION: The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Association of Bicuspid aortic valve morphology and aortic root dimensions: a substudy of the aortic stenosis progression observation measuring effects of rosuvastatin (ASTRONOMER) study.

BACKGROUND: Bicuspid aortic valve (BAV) is the leading cause of aortic stenosis in patients younger than the age of 50. A classification scheme of BAVs is based upon leaflet orientation: Type I (fusion of right and left coronary cusps) and Type II (fusion of right and noncoronary cusps). The correlation between BAV leaflet orientation and aortic root pathology however remains ill defined. OBJECTIVE: The objective was to describe a potential relationship between BAV leaflet morphology and aortic root measurements in the ASTRONOMER study, a multicenter study to assess the effect of rosuvastatin on the progression of AS. METHODS: BAV morphology was classified as Type I or Type II orientation based on the parasternal short-axis view. Echo measurements including left ventricular and aortic root dimensions were obtained. RESULTS: The study population included 89 patients (56 +/- 11 years; 44 males). There were 63 patients with Type I and 26 patients with Type II BAV. Baseline demographics, hemodynamics, and left heart dimensions were similar between both groups. Patients with Type I BAV had larger aortic annulus and ascending root dimensions compared to those patients with Type II BAV (P < 0.05). CONCLUSION: In patients with mild to moderate aortic stenosis due to a BAV, the presence of Type I valve orientation was associated with significantly greater aortic root parameters compared to Type II valve orientation.

Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN.

PURPOSE:  The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. METHODS:  We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. RESULTS:  A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. CONCLUSION:  This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.

Association of mitral annular calcification and aortic valve morphology: a substudy of the aortic stenosis progression observation measuring effects of rosuvastatin (ASTRONOMER) study.

AIMS: Mitral annular calcification (MAC) is characterized by calcium and lipid deposition in the annular fibrosa of the mitral valve. Although individuals with MAC are at increased risk of cardiovascular events, little is known about the significance of this finding in patients with concurrent aortic stenosis (AS). The aim of this study was to describe the association of baseline MAC and aortic valve morphology in asymptomatic patients enrolled in the ASTRONOMER study, a multicentre study to assess the effect of Rosuvastatin on the progression of AS. METHODS AND RESULTS: At baseline, transthoracic echocardiography was performed with two-dimensional and Doppler imaging following a standardized protocol. Echo measurements including left ventricular (LV) dimensions and aortic root dimensions were obtained according to the ASE recommendations. MAC was identified by bright echoes at the base of the mitral leaflets or annulus on 2D imaging, and aortic valve calcification by visualization of bright echoes on the aortic valve leaflets. The degree of calcification was semi-quantitated from absent to severe. The study population included 219 patients (57 +/- 14 years; 129 males), divided into two pre-specified categories; bicuspid (n = 133) and tricuspid (n = 86) aortic valves. Baseline LV dimensions, aortic valve haemodynamics, and cholesterol profiles were similar between the two groups at baseline. Individuals with tricuspid aortic valves were older, more hypertensive, with higher degrees of MAC and AV calcification (P < 0.001). The higher degree of MAC persisted in patients with tricuspid AV after adjustment for age and systolic blood pressure (P = 0.004). CONCLUSION: In patients with asymptomatic mild to moderate AS, MAC is more prevalent in those individuals with tricuspid AV, independent of age, and systolic blood pressure. Whether the degree of MAC may be a surrogate for atherosclerosis, and predict the subset of patients who will respond to statin therapy in preventing the progression of AS, remains to be determined.