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.

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.

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.

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.

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.

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.

Echo-SyncNet: Self-Supervised Cardiac View Synchronization in Echocardiography.

In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any human supervision or external inputs. The proposed framework takes advantage of two types of supervisory signals derived from the input data: spatiotemporal patterns found between the frames of a single cine (intra-view self-supervision) and interdependencies between multiple cines (inter-view self-supervision). The combined supervisory signals are used to learn a feature-rich and low dimensional embedding space where multiple echo cines can be temporally synchronized. Two intra-view self-supervisions are used, the first is based on the information encoded by the temporal ordering of a cine (temporal intra-view) and the second on the spatial similarities between nearby frames (spatial intra-view). The inter-view self-supervision is used to promote the learning of similar embeddings for frames captured from the same cardiac phase in different echo views. We evaluate the framework with multiple experiments: 1) Using data from 998 patients, Echo-SyncNet shows promising results for synchronizing Apical 2 chamber and Apical 4 chamber cardiac views, which are acquired spatially perpendicular to each other; 2) Using data from 3070 patients, our experiments reveal that the learned representations of Echo-SyncNet outperform a supervised deep learning method that is optimized for automatic detection of fine-grained cardiac cycle phase; 3) We go one step further and show the usefulness of the learned representations in a one-shot learning scenario of cardiac key-frame detection. Without any fine-tuning, key frames in 1188 validation patient studies are identified by synchronizing them with only one labeled reference cine. We do not make any prior assumption about what specific cardiac views are used for training, and hence we show that Echo-SyncNet can accurately generalize to views not present in its training set. Project repository: github.com/fatemehtd/Echo-SyncNet>.

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.

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.

Precautions and Procedures for Coronary and Structural Cardiac Interventions During the COVID-19 Pandemic: Guidance from Canadian Association of Interventional Cardiology.

The globe is currently in the midst of a COVID-19 pandemic, resulting in significant morbidity and mortality. This pandemic has placed considerable stress on health care resources and providers. This document from the Canadian Association of Interventional Cardiology- Association Canadienne de Cardiologie d'intervention, specifically addresses the implications for the care of patients in the cardiac catheterization laboratory (CCL) in Canada during the COVID-19 pandemic. The key principles of this document are to maintain essential interventional cardiovascular care while minimizing risks of COVID-19 to patients and staff and maintaining the overall health care resources. As the COVID-19 pandemic evolves, procedures will be increased or reduced based on the current level of restriction to health care services. Although some consistency across the country is desirable, provincial and regional considerations will influence how these recommendations are implemented. We believe the framework and recommendations in this document will provide crucial guidance for clinicians and policy makers on the management of coronary and structural procedures in the CCL as the COVID-19 pandemic escalates and eventually abates.

On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context of 2D echocardiography (echo), which is a routine procedure for detecting cardiovascular disease at point-of-care. Echo imaging quality and acquisition time is highly dependent on the operator's experience level. Recent developments have shown the possibility of automating echo image quality quantification by mapping an expert's assessment of quality to the echo image via deep learning techniques. Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy. Here, we aim to model the intra-observer variability in echo quality assessment as an aleatoric uncertainty modelling regression problem with the introduction of a novel method that handles the regression problem with categorical labels. A key feature of our design is that only a single forward pass is sufficient to estimate the level of uncertainty for the network output. Compared to the 0.11 ± 0.09 absolute error (in a scale from 0 to 1) archived by the conventional regression method, the proposed method brings the error down to 0.09 ± 0.08, where the improvement is statistically significant and equivalents to 5.7% test accuracy improvement. The simplicity of the proposed approach means that it could be generalized to other applications of deep learning in medical imaging, where there is often uncertainty in clinical labels.

Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training.

PURPOSE: Left ventricular ejection fraction (LVEF) is one of the key metrics to assess the heart functionality, and cardiac ultrasound (echo) is a standard imaging modality for EF measurement. There is an emerging interest to exploit the point-of-care ultrasound (POCUS) usability due to low cost and ease of access. In this work, we aim to present a computationally efficient mobile application for accurate LVEF estimation. METHODS: Our proposed mobile application for LVEF estimation runs in real time on Android mobile devices that have either a wired or wireless connection to a cardiac POCUS device. We propose a pipeline for biplane ejection fraction estimation using apical two-chamber (AP2) and apical four-chamber (AP4) echo views. A computationally efficient multi-task deep fully convolutional network is proposed for simultaneous LV segmentation and landmark detection in these views, which is integrated into the LVEF estimation pipeline. An adversarial critic model is used in the training phase to impose a shape prior on the LV segmentation output. RESULTS: The system is evaluated on a dataset of 427 patients. Each patient has a pair of captured AP2 and AP4 echo studies, resulting in a total of more than 40,000 echo frames. The mobile system reaches a noticeably high average Dice score of 92% for LV segmentation, an average Euclidean distance error of 2.85 pixels for the detection of anatomical landmarks used in LVEF calculation, and a median absolute error of 6.2% for LVEF estimation compared to the expert cardiologist's annotations and measurements. CONCLUSION: The proposed system runs in real time on mobile devices. The experiments show the effectiveness of the proposed system for automatic LVEF estimation by demonstrating an adequate correlation with the cardiologist's examination.

Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss.

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames. The proposed architectures integrate convolution neural networks (CNNs)-based image feature extraction model and recurrent neural networks (RNNs) to model temporal dependencies between each frame in a sequence. We explore two CNN architectures: DenseNet and ResNet, and four RNN architectures: long short-term memory, bi-directional LSTM, gated recurrent unit (GRU), and Bi-GRU, and compare the performance of these models. The optimal deep learning model consists of a DenseNet and GRU trained with the proposed loss function. On average, we achieved 0.20 and 1.43 frame mismatch for the ED and ES frames, respectively, which are within reported inter-observer variability for the manual detection of these frames.

Clinical effectiveness of a systematic "pill-in-the-pocket" approach for the management of paroxysmal atrial fibrillation.

BACKGROUND: For patients with symptomatic, sustained atrial fibrillation (AF), a "pill-in-the-pocket" antiarrhythmic drug (PIP-AAD) strategy has been proposed to reduce emergency department (ED) use. OBJECTIVE: To assess the clinical utility of a protocolled PIP-AAD approach within contemporary practice. METHODS: Consecutive patients who hemodynamically tolerated symptomatic, sustained AF were prospectively managed with the PIP-AAD strategy. All patients were given an atrioventricular nodal blocker 30 minutes prior to a single oral dose of a class Ic antiarrhythmic drug. If the initial PIP-AAD in the ED was efficacious and tolerated, PIP-AADs were given out of hospital for subsequent sustained AF episodes. Usage and complications were systematically recorded. RESULTS: During a median follow-up period of 565 days, 43 of 80 patients presented to the ED for initial PIP-AAD. Sinus rhythm was restored without complication in 30 of 43 patients. The reasons for initial PIP-AAD failure were inefficacy (6 patients), significant hypotension (4 patients), conversion to flutter necessitating cardioversion (2 patients), and syncopal conversion pause (1 patient). For the 30 patients with successful initial PIP-AAD, 159 out-of-hospital PIP-AAD treatments occurred (mean 5.3 ± SD 1.3 per patient). Compared with ED visits in the period prior to PIP-AAD initiation, there was a significant reduction in visits (2.6 ± 3.0 vs. 0.4±0.9 ED visits per patient, P < .001) and the need for cardioversion (2.3 ± 3.1 vs. 0.0 ± 0.2 treatments per patient, P < .001). Adverse events associated with out-of-hospital PIP-AAD include presyncope (3 of 30 patients), syncope necessitating pacemaker implantation (1 patient), and conversion to flutter (1 patient). CONCLUSION: Out-of-hospital PIP-AAD can be an effective for highly selected patients; however, the rates of treatment failure and adverse events are clinically relevant, which limits the widespread application of a PIP-AAD approach.

Doppler Parameters Derived from Transthoracic Echocardiography Accurately Detect Bioprosthetic Mitral Valve Dysfunction.

BACKGROUND: Detecting bioprosthetic mitral valve dysfunction on transthoracic echocardiography can be challenging because of acoustic shadowing of regurgitant jets and a wide normal range of transvalvular gradients. Several studies in mechanical mitral valves have demonstrated the utility of the transthoracically derived parameters E (peak early mitral inflow velocity), pressure half-time, and the ratio of mitral inflow velocity-time integral (VTIMV) to left ventricular outflow tract velocity-time integral (VTILVOT) in detecting significant prosthetic dysfunction. Uncertainty exists as to their applicability and appropriate cutoff levels in bioprosthetic valves. This study was designed to establish the accuracy and appropriate normal limits of routinely collected transthoracic Doppler parameters when used to assess bioprosthetic mitral valve function. METHODS: A total of 135 clinically stable patients with bioprosthetic mitral valves who had undergone both transthoracic and transesophageal echocardiography within a 6-month period were retrospectively identified from the past 11 years of the echocardiography database. Transthoracic findings were labeled as normal (n = 81), regurgitant (n = 44), or stenotic (n = 10) according to the patient's transesophageal echocardiographic findings. Univariate and multivariate analyses were performed to identify Doppler parameters that detected dysfunction; then receiver operating characteristic curves were created to establish appropriate normal cutoff levels. RESULTS: The VTIMV/VTILVOT ratio was the most accurate Doppler parameter at detecting valvular dysfunction, with a ratio of >2.5 providing sensitivity of 100% and specificity of 95%. E > 1.9 m/sec was slightly less accurate (93% sensitivity, 72% specificity), while a pressure half-time of >170 msec had both 100% specificity and sensitivity for detecting significant bioprosthetic mitral valve stenosis, (although it did not differentiate between regurgitant and normal). CONCLUSIONS: This study demonstrates that Doppler parameters derived from transthoracic echocardiography can accurately detect bioprosthetic mitral valve dysfunction. These parameters, particularly a VTIMV/VTILVOT ratio of >2.5, are a sensitive way of selecting patients to undergo more invasive examination with transesophageal echocardiography.

Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View.

Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert's manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Simultaneous Analysis of 2D Echo Views for Left Atrial Segmentation and Disease Detection.

We propose a joint information approach for automatic analysis of 2D echocardiography (echo) data. The approach combines a priori images, their segmentations and patient diagnostic information within a unified framework to determine various clinical parameters, such as cardiac chamber volumes, and cardiac disease labels. The main idea behind the approach is to employ joint Independent Component Analysis of both echo image intensity information and corresponding segmentation labels to generate models that jointly describe the image and label space of echo patients on multiple apical views, instead of independently. These models are then both used for segmentation and volume estimation of cardiac chambers such as the left atrium and for detecting pathological abnormalities such as mitral regurgitation. We validate the approach on a large cohort of echoes obtained from 6,993 studies. We report performance of the proposed approach in estimation of the left-atrium volume and detection of mitral-regurgitation severity. A correlation coefficient of 0.87 was achieved for volume estimation of the left atrium when compared to the clinical report. Moreover, we classified patients that suffer from moderate or severe mitral regurgitation with an average accuracy of 82%.

Prophylactic magnesium does not prevent atrial fibrillation after cardiac surgery: a meta-analysis.

BACKGROUND: Atrial fibrillation (AF) is a common complication after cardiac surgery. Previous meta-analyses have concluded prophylactic magnesium (Mg) prevents postoperative AF, although with a significant degree of heterogeneity among included studies. Recently, the largest randomized, controlled trial published to date (but not included in prior published meta-analyses) concluded that Mg sulfate is not protective against AF after cardiac surgery. The objective of this study was to conduct a new meta-analysis including the results of new Mg trials not included in previous meta-analyses, and to investigate the potential causes and effects of significant heterogeneity observed in previously published meta-analyses. METHODS: The MEDLINE, EMBASE, and CENTRAL databases were searched for relevant studies published up to March 31, 2012. Pooled odds ratios of occurrence of AF were calculated using random-effects models. Heterogeneity was assessed as significant using the I(2) statistic. RESULTS: Egger's and funnel plots demonstrated biases toward stronger and more positive effects of Mg in smaller studies. When the analysis was restricted to the five double-blind, intention-to-treat studies in which AF was the primary outcome (Mg arm, n = 710; control arm, n = 713), Mg did not prevent postoperative AF (odds ratio, 0.94; p = 0.77), and heterogeneity was no longer significant (I(2) = 40%; p = 0.15). CONCLUSIONS: This meta-analysis, restricted to well-conducted trials, does not support the prophylactic use of Mg to prevent AF after cardiac surgery. Prior meta-analyses have drawn conclusions from simple random-effects models with significant heterogeneity. However, this approach leaves important residual heterogeneity and overemphasizes the strongly positive effects of smaller studies.

Outcome of patients aged >or=75 years in the SHould we emergently revascularize Occluded Coronaries in cardiogenic shocK (SHOCK) trial: do elderly patients with acute myocardial infarction complicated by cardiogenic shock respond differently to emergent revascularization?

BACKGROUND: In the SHOCK trial, the group of patients aged >or=75 years did not appear to derive the mortality benefit from early revascularization (ERV) versus initial medical stabilization (IMS) that was seen in patients aged <75 years. We sought to determine the reason for this finding by examining the baseline characteristics and outcomes of the 2 treatment groups by age. METHODS: Patients with cardiogenic shock (CS) secondary to left ventricular (LV) failure were randomized to ERV within 6 hours or to a period of IMS. We compared the characteristics by treatment group of patients aged >or=75 years and of their younger counterparts. RESULTS: Of the 56 enrolled patients aged >or=75 years, those assigned to ERV had lower LV ejection fraction at baseline than IMS-assigned patients (27.5% +/- 12.7% vs 35.6% +/- 11.6%, P = .051). In the elderly ERV and IMS groups, 54.2% and 31.3%, respectively, were women ( P = .105) and 62.5% and 40.6%, respectively, had an anterior infarction (P = .177). The 30-day mortality rate in the ERV group was 75.0% in patients aged >or=75 years and 41.4% in those aged <75 years. In the IMS group, 30-day mortality was 53.1% for those aged >or=75 years, similar to the 56.8% for patients aged <75 years. CONCLUSIONS: Overall, the elderly randomized to ERV did not have better survival than elderly IMS patients. Despite the strong association of age and death post-CS, elderly patients assigned to IMS had a 30-day mortality rate similar to that of IMS patients aged <75 years, suggesting that this was a lower-risk group with more favorable baseline characteristics. The lack of apparent benefit from ERV in elderly patients in the SHOCK trial may thus be due to differences in important baseline characteristics, specifically LV function, and play of chance arising from the small sample size. Therefore, the SHOCK trial overall finding of a 12-month survival benefit for ERV should be viewed as applicable to all patients, including those >or=75 years of age, with acute myocardial infarction complicated by CS.