Automated interpretation of systolic and diastolic function on the echocardiogram:a multicohort study

Background: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. Methods: We developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the US-based EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. Findings: In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0·91 to 0·99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9–25 mL for left ventricular volumes, 6–10% for left ventricular ejection fraction (LVEF), and 1·8–2·2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0·90–0·92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0·91–0·91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. Interpretation: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally. Funding: A*STAR Biomedical Research Council and A*STAR Exploit Technologies

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)

This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes

Computer-assisted auscultation as a screening tool for cardiovascular disease : a cross-sectional study

Includes synopsis.Includes bibliographical references.Cardiac auscultation is inherently qualitative, highly subjective and requires considerable skill and experience. Computer- assisted auscultation (CAA) is an objective referral-decision support tool that aims to minimise inappropriate referrals. This study evaluated the sensitivity and specificity of 2 CAA systems, Cardioscan® and Sensi®, in detecting echo-confirmed cardiac abnormalities in 79 consecutive patients referred for assessment to a tertiary cardiac clinic. CAA demonstrated suboptimal sensitivity and specificity in detecting cardiac abnormalities in children and adults. As both systems demonstrate 100% sensitivity in detecting acyanotic heart disease, and theoretically carry significant potential in resource-limited settings, further development of current technologies to improve sensitivity and specificity for clinical applications is still warranted

Outcomes of asymptomatic and symptomatic rheumatic heart disease

Includes bibliographical referencesRheumatic Heart Disease (RHD) is a leading cause of heart disease in children and young adults in the developing world, with significant associated morbidity and mortality. Early secondary prophylaxis may retard the deleterious progression from its antecedent, acute rheumatic fever to permanent heart valve damage, and thus several echocardiographic screening programmes to detect asymptomatic RHD and institute early prophylaxis have been conducted. While effective interventions are available for ameliorating the effects of RHD, research on their use in different settings is scant. Key questions remain regarding the natural history of asymptomatic RHD and the optimal method for early detection. In addition, there is a lack of contemporary estimates of mortality and morbidity among the symptomatic population in the developing world. The primary purpose of the thesis was to determine the outcomes of asymptomatic and symptomatic RHD. More specifically, I sought to quantify the incidence, prevalence and outcomes of RHD in South Africa over the past two decades, determine the natural history of asymptomatic RHD and validate a focused protocol for screening in schoolchildren from Cape Town. In addition, I determined the baseline characteristics, prevalent sequelae and gaps in evidence-based implementation in children and adults from14 developing countries. Finally, I investigated the independent predictors for mortality and morbidity of RHD over a two-year period in patients from Cape Town, South Africa. My thesis has five key findings. Firstly, a systematic review of the literature showed that the incidence and prevalence of RHD over the past two decades in South Africa remains high, although there is evidence of falling cause-specific mortality at a population level. Secondly, asymptomatic RHD has a variable natural history that ranges from regression to a normal state, to persistence of disease, and progression to symptomatic RHD. Thirdly, a focused hand-held echocardiography protocol shows promising levels of sensitivity and specificity for detecting subclinical RHD. Fourthly, the baseline data from the global rheumatic heart disease registry demonstrates significant gaps in the implementation of medical and surgical interventions of proven effectiveness in low- and middle-income countries. Finally, the annual mortality rate for children and adults with RHD in Cape Town over a two-year period is 4.1%with cardiovascular events occurring at a rate of 0.18 events per patient per year. The findings encapsulated in this thesis have important implications for policy, practice and research related to the management of asymptomatic and symptomatic RHD in the world