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Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements.

Objective: The right ventricle receives less attention than its left counterpart in echocardiography research, practice and development of automated solutions. In the work described here, we sought to determine that the deep learning methods for automated segmentation of the left ventricle in 2-D echocardiograms are also valid for the right ventricle. Additionally, here we describe and explore a keypoint detection approach to segmentation that guards against erratic behavior often displayed by segmentation models., Methods: We used a data set of echo images focused on the right ventricle from 250 participants to train and evaluate several deep learning models for segmentation and keypoint detection. We propose a compact architecture (U-Net KP) employing the latter approach. The architecture is designed to balance high speed with accuracy and robustness., Results: All featured models achieved segmentation accuracy close to the inter-observer variability. When computing the metrics of right ventricular systolic function from contour predictions of U-Net KP, we obtained the bias and 95% limits of agreement of 0.8 ± 10.8% for the right ventricular fractional area change measurements, -0.04 ± 0.54 cm for the tricuspid annular plane systolic excursion measurements and 0.2 ± 6.6% for the right ventricular free wall strain measurements. These results were also comparable to the semi-automatically derived inter-observer discrepancies of 0.4 ± 11.8%, -0.37 ± 0.58 cm and -1.0 ± 7.7% for the aforementioned metrics, respectively., Conclusion: Given the appropriate data, automated segmentation and quantification of the right ventricle in 2-D echocardiography are feasible with existing methods. However, keypoint detection architectures may offer higher robustness and information density for the same computational cost., Competing Interests: Conflict of interest S.A.A. holds a full-time position at GE Vingmed Ultrasound AS, Horten, Norway. L.L. is a part-time consultant at GE Vingmed Ultrasound AS, Horten, Norway., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Continuous technologic development and updated recommendations for image acquisitions creates a need to update the current normal reference ranges for echocardiography. The best method of indexing cardiac volumes is unknown., Objectives: The authors used 2- and 3-dimensional echocardiographic data from a large cohort of healthy individuals to provide updated normal reference data for dimensions and volumes of the cardiac chambers as well as central Doppler measurements., Methods: In the fourth wave of the HUNT (Trøndelag Health) study in Norway 2,462 individuals underwent comprehensive echocardiography. Of these, 1,412 (55.8% women) were classified as normal and formed the basis for updated normal reference ranges. Volumetric measures were indexed to body surface area and height in powers of 1 to 3., Results: Normal reference data for echocardiographic dimensions, volumes, and Doppler measurements were presented according to sex and age. Left ventricular ejection fraction had lower normal limits of 50.8% for women and 49.6% for men. According to sex-specific age groups, the upper normal limits for left atrial end-systolic volume indexed to body surface area ranged from 44 mL/m 2 to 53 mL/m 2 , and the corresponding upper normal limit for right ventricular basal dimension ranged from 43 mm to 53 mm. Indexing to height raised to the power of 3 accounted for more of the variation between sexes than indexing to body surface area., Conclusions: The authors present updated normal reference values for a wide range of echocardiographic measures of both left- and right-side ventricular and atrial size and function from a large healthy population with a wide age-span. The higher upper normal limits for left atrial volume and right ventricular dimension highlight the importance of updating reference ranges accordingly following refinement of echocardiographic methods., Competing Interests: Funding Support and Author Disclosures This study was funded by the Liaison Committee for Education, Research, and Innovation in Central Norway and grants from the Simon Fougner Hartmann Family Fund, Denmark. Drs Grue, Olaisen, Løvstakken, and Dalen hold positions at the Center for Innovative Ultrasound Solutions, where GE Ultrasound is one of the institutional partners. Dr Løvstakken is a part-time consultant for GE Ultrasound. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Automatic analyses of echocardiograms may support inexperienced users in quantifying left ventricular (LV) function. We have developed an algorithm for fully automatic measurements of mitral annular plane systolic excursion (MAPSE) and mitral annular systolic (S') and early diastolic (e') peak velocities. We aimed to study the influence of user experience of automatic measurements of these indices in echocardiographic recordings acquired by medical students and clinicians., Methods: We included 75 consecutive patients referred for echocardiography at a university hospital. The patients underwent echocardiography by clinicians (cardiologists, cardiology residents and sonographers), who obtained manual reference measurements of MAPSE by M-mode and of S' and e' by colour tissue Doppler imaging (cTDI). Immediately after, each patient was examined by 1 of 39 medical students who were instructed in image acquisition on the day of participation. Each student acquired cTDI recordings from 1 to 4 patients. All cTDI recordings by students and clinicians were analysed for MAPSE, S' and e' using a fully automatic algorithm. The automatic measurements were compared to the manual reference measurements., Results: Correct tracking of the mitral annulus was feasible in 50 (67%) and 63 (84%) of the students' and clinicians' recordings, respectively (p = 0.007). Image quality was highest in the clinicians' recordings. Mean difference ± standard deviation of the automatic measurements of the students' recordings compared to the manual reference was - 0.0 ± 2.0 mm for MAPSE, 0.3 ± 1.1 cm/s for S' and 0.6 ± 1.4 cm/s for e'. The corresponding intraclass correlation coefficients for MAPSE, S' and e' were 0.85 (good), 0.89 (good) and 0.92 (excellent), respectively. Automatic measurements from the students' and clinicians' recordings were in similar agreement with the reference when mitral annular tracking was correct., Conclusions: In case of correct tracking of the mitral annulus, the agreement with reference for the automatic measurements was overall good. Low image quality reduced feasibility. Adequate image acquisition is essential for automatic analyses of LV function indices, and thus, appropriate education of the operators is mandatory. Automatic measurements may help inexperienced users of ultrasound, but do not remove the need for dedicated education and training.

Background: Automatic quantification of left ventricular (LV) function could enhance workflow for cardiologists and assist inexperienced clinicians who perform focused cardiac ultrasound. We have developed an algorithm for automatic measurements of the mitral annular plane systolic excursion (MAPSE) and peak velocities in systole (S') and early (e') and late (a') diastole. We aimed to establish normal reference values for the automatic measurements and to compare them with manual measurements., Methods and Results: Healthy participants (n = 1157, 52.5% women) from the HUNT3 cross-sectional population study in Norway were included. The mean age ± standard deviation (SD) was 49 ± 14 (range: 19-89) years. The algorithm measured MAPSE, S', e', and a' from apical 4-chamber color tissue Doppler imaging (cTDI) recordings. The manual measurements were obtained by two echocardiographers, who measured MAPSE by M-mode and the velocities by cTDI. For men and women, age-specific reference values were created for groups (mean ± 1.96SD) and by linear regression (mean, 95% prediction interval). Age was negatively correlated with MAPSE, S', and e' and positively correlated with a'. There were small differences between genders. Normal reference ranges were created. The coefficients of variation between automatic and manual measurements ranged from 5.5% (S') to 11.7% (MAPSE)., Conclusion: Normal reference values for automatic measurements of LV function indices are provided. The automatic measurements were in line with the manual measurements. Implementing automatic measurements and comparison with normal ranges in ultrasound scanners can allow for quick and precise interpretation of LV function., (© 2019 The Authors. Echocardiography published by Wiley Periodicals, Inc.)

The purpose of the study described here was to evaluate an automatic algorithm for detection of left ventricular dysfunction, based on measurements of mitral annular motion indices from color tissue Doppler apical four-chamber recordings. Two hundred twenty-one patients, among whom 49 had systolic and 11 had diastolic dysfunction, were included. Echocardiographic evaluation by cardiologists was the reference. Twenty patients were also examined by medical students. The ability of the indices to detect systolic and diastolic dysfunction were compared in receiver operating characteristic analyses, and the agreement between automatic and reference measurements was evaluated. Mitral annular plane systolic excursion ≤10 mm detected left ventricular dysfunction with 82% specificity, 76% specificity, 56% positive predictive value and 92% negative predictive value. The automatic measurements acquired from expert recordings better agreed better with the reference than those acquired from student recordings. We conclude that automatic measurements of systolic mitral annular motion indices can be helpful in detection of left ventricular dysfunction., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Assessment of cardiac function by echocardiography is challenging for nonexperts. In a patient with dyspnea, quantification of the mitral annular excursion (MAE) and velocities is important for the diagnosis of heart failure. The displacement of the atrioventricular (AV) plane is a good indicator of systolic left ventricular function, while the peak velocities give supplementary information about the systolic and diastolic function. By measuring these parameters automatically, a preliminary diagnosis can be given by the nonexpert. We propose an automatic algorithm to localize the mitral annular points in an apical four-chamber view and estimate the MAE, as well as the systolic, early diastolic, and late diastolic tissue peak velocities, by using a deformable ventricle model for orientation and tissue Doppler data for tracking. Automatic parameter estimates from 367 tissue Doppler recordings were compared to reference measurements by experienced cardiologists to assess the accuracy of the estimation, as well as the ability to correctly detect reduced MAE, which we defined as less than 10 mm. The dataset consisted of 200 recordings from a patient population and 167 healthy from a population study. When considering the average of the septal and lateral values, the estimation error for the MAE had a standard deviation of 2.1 mm, which was reduced to 1.9 mm when excluding recordings for which the automatic segmentation failed to locate the AV plane (41 recordings). The corresponding standard deviations for the peak velocities were around 1 cm/s. The classification of MAE was correct in 90% of the cases and had a sensitivity of 83% and a specificity of 92%. We conclude that the algorithm has good accuracy and note that the estimation error for the MAE was comparable to interobserver and methodology agreements reported in the literature.