Validity of automated measurement of left ventricular ejection fraction and volume using the Philips EPIQ system

Impaired β 2 -adrenergic endothelium-dependent vasodilation in patients previously hospitalized with coronavirus disease 2019

AIM

The pulse wave response to salbutamol (PWRS) - change in augmentation index (AIx) - provides a means to assess endothelial vasodilator function in vivo . Endothelial dysfunction plays a relevant role in the pathogenesis of hypertension and cardiovascular disease and appears to underlie many of the complications of coronavirus disease 2019 (COVID-19). However, to what degree this persists after recovery is unknown.

METHODS

Individuals previously hospitalized with COVID-19, those recovered from mild symptoms and seronegative controls with well known risk factors for endothelial dysfunction were studied. To assess the involvement of nitric oxide-cyclic guanosine monophosphate pathway (NO-cGMP) on PWRS, sildenafil was also administrated in a subsample.

RESULTS

One hundred and one participants (60 men) aged 47.8 ± 14.1 (mean ± SD) years of whom 33 were previously hospitalized with COVID-19 were recruited. Salbutamol had minimal effect on haemodynamics including blood pressure and heart rate. It reduced AIx in controls ( n  = 34) and those recovered from mild symptoms of COVID-19 ( n  = 34) but produced an increase in AIx in those previously hospitalized: mean change [95% confidence interval] -2.85 [-5.52, -0.188] %, -2.32 [-5.17,0.54] %, and 3.03 [0.06, 6.00] % for controls, those recovered from mild symptoms and those previously hospitalized, respectively ( P  = 0.001). In a sub-sample ( n  = 22), sildenafil enhanced PWRS (change in AIx 0.05 [-2.15,2.24] vs. -3.96 [-7.01. -2.18], P  = 0.006) with no significant difference between hospitalized ( n  = 12) and nonhospitalized participants ( n  = 10).

CONCLUSIONS

In patients previously hospitalized with COVID-19, there is long-lasting impairment of endothelial function as measured by the salbutamol-induced stimulation of the NO-cGMP pathway that may contribute to cardiovascular complications.

Fully Automated Mouse Echocardiography Analysis Using Deep Convolutional Neural Networks

Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Though echo is a very valuable tool, the image analysis is a time consuming, resource demanding process, and is susceptible to inter-reader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and inter-reader variability in the field of medical imaging. In the present study, we developed a fully automated tool - Mouse Echo Neural Net (MENN) - for the analysis of both long axis brightness (B)-mode and short axis motion (M)-mode images of the left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85 to 0.99) and good to excellent agreement between automated and manual analyses. Further inter-reader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of inter-reader variability in manual analysis.

Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device

Aims

We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population.

Methods and results

We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85; r = 0.87, P < 0.001; mean bias -1.42% with limits of agreement 14.5%, respectively). Detection of abnormal LV function (EF < 50%) by autoEF algorithm was feasible with sensitivity 90% (95% CI 75-97%), specificity 87% (95% CI 76-94%), PPV 81% (95% CI 66-91%), NPV 93% (95% CI 83-98%), and a total diagnostic accuracy of 88%. Test-retest reliability was excellent (ICC = 0.91, P < 0.001; r = 0.91, P < 0.001; mean difference ± SD: 0.54% ± 5.27%, P = 0.308) and MDC for LVEF measurement by autoEF was calculated at 4.38%.

Conclusion

Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.

Feasibility and Reliability of Automatic Quantitative Analyses of Mitral Annular Plane Systolic Excursion by Handheld Ultrasound Devices: A Pilot Study

OBJECTIVES

Handheld ultrasound devices (HUDs) have previously been limited to grayscale imaging without options for left ventricle (LV) quantification. We aimed to study the feasibility and reliability of automatic measurements of mitral annular plane systolic excursion (MAPSE) by HUDs.

METHODS

An algorithm that automatically measured MAPSE from live grayscale recordings was implemented in a HUD. Twenty patients at a university hospital were examined by either a cardiologist or a sonographer. Standard echocardiography using a high-end scanner was performed. The apical 4-chamber view was recorded 4 times by both echocardiography and the HUD. MAPSE was measured by M-mode and color tissue Doppler (cTD) during echocardiography and automatically by the HUD.

RESULTS

The automatic method underestimated mean MAPSE ± SD versus M-mode (9.6 ± 2.2 versus 10.9 ± 2.6 mm; difference, 1.2 ± 1.4 mm, P < .005). The difference between the automatic and cTD measurements was not significant (0.8 ± 1.8 mm; P = .073). The intraclass correlation coefficients (ICCs) between automatic and M-mode measurements was 0.85, and 0.81 for cTD measurements. There was good agreement between the methods, and the intra- and inter-rater ICCs were excellent for all methods (≥0.86).

CONCLUSIONS

In this novel study evaluating automatic quantification of LV longitudinal function by HUD, we showed the high feasibility and reliability of the method. Compared to M-mode imaging, the automatic method underestimated MAPSE by 8% to 10%, but the difference with cTD imaging was nonsignificant. We conclude that this study's method for automatic quantitative assessment of LV function can be integrated in HUDs.

Left ventricular ejection fraction using manual and semi-automated biplane method of discs in very preterm infants

BACKGROUND

Biplane left ventricular ejection fraction (LVEF) is a valuable echocardiographic parameter for assessment of LV systolic pump efficiency in adults and children, but not often reported in preterm infants. The primary aim of this study was to longitudinally measure biplane LVEF in very preterm infants during the neonatal intensive care period. Secondary aim was to compare manual and semi-automatic determination of LVEF for agreement and variability.

METHODS

Stable preterm infants less than 30 weeks gestation were scanned on day 3, day 28, and at 36 weeks postmenstrual age. The LV endocardium was traced manually and semi-automatically using integrated speckle tracking software in apical 4-chamber and apical 3-chamber images to obtain end-diastolic volume and end-systolic volume, and calculate LVEF. Agreement between methods and variability within and between observers was determined using an interclass correlation coefficient (ICC) and Bland-Altman analysis.

RESULTS

Sixty-six preterm infants with a mean birth weight of 1100 (239) g were analyzed. The average manual biplane LVEF was 58 (3)%, 59 (3)%, and 55 (4)% at the three respective time points. Manual LVEF showed good agreement with semi-automatic LVEF (ICC 0.76) with a small bias of -1.5 (3.0)%. Interobserver variability of LVEF improved with semi-automatic tracing of the LV endocardial border (ICC manual 0.68 vs semi-automatic 0.80).

CONCLUSION

Left ventricular systolic pump efficiency in preterm infants remains stable during the neonatal intensive care period. Semi-automatic biplane LVEF has less interobserver variability and can be used interchangeably with manual biplane LVEF.

Post-exertional increase in first-phase ejection fraction in recreational marathon runners

OBJECTIVES

Running a marathon has been equivocally associated with acute changes in cardiac performance. First-phase ejection fraction is a novel integrated echocardiographic measure of left ventricular contractility and systo-diastolic coupling which has never been studied in the context of physical activity. The aim of this study was to assess first-phase ejection fraction following recreational marathon running along with standard echocardiographic indices of systolic and diastolic function.Design and participants: Runners (n = 25, 17 males), age (mean ± standard deviation) 39 ± 9 years, were assessed before and immediately after a marathon race which was completed in 4 h, 10 min ± 47 min.

MAIN OUTCOME MEASURES

Central hemodynamics were estimated with applanation tonometry; cardiac performance was assessed using standard M-mode two-dimensional Doppler, tissue-doppler imaging and speckle-tracking echocardiography. First-phase ejection fraction was calculated as the percentage change in left ventricular volume from end-diastole to the time of peak aortic blood flow.

RESULTS

Conventional indices of systolic function and cardiac performance were similar pre- and post-race while aortic systolic blood pressure decreased by 9 ± 8 mmHg (P < 0.001) and first-phase ejection fraction increased by approximately 48% from 16.3 ± 3.9% to 22.9 ± 2.5% (P < 0.001). The ratio of left ventricular transmitral Doppler early velocity (E) to tissue-doppler imaging early annular velocity (e') increased from 5.1 ± 1.8 to 6.2 ± 1.3 (P < 0.01).

CONCLUSION

In recreational marathon runners, there is a marked increase in first-phase ejection fraction after the race despite no other significant change in cardiac performance or conventional measure of systolic function. More detailed physiological studies are required to elucidate the mechanism of this increase.

Speckle tracking quantification of lung sliding for the diagnosis of pneumothorax: a multicentric observational study

PURPOSE

Lung ultrasound is used for the diagnosis of pneumothorax, based on lung sliding abolition which is a qualitative and operator-dependent assessment. Speckle tracking allows the quantification of structure deformation over time by analysing acoustic markers. We aimed to test the ability of speckle tracking technology to quantify lung sliding in a selected cohort of patients and to observe how the technology may help the process of pneumothorax diagnosis.

METHODS

We performed retrospectively a pleural speckle tracking analysis on ultrasound loops from patients with pneumothorax. We compared the values measured by two observers from pneumothorax side with contralateral normal lung side. The receiver operating characteristic (ROC) curve was constructed to evaluate the performance of maximal pleural strain to detect the lung sliding abolition. Diagnosis performance and time to diagnosis between B-Mode and speckle tracking technology were compared from a third blinded observer.

RESULTS

We analysed 104 ultrasound loops from 52 patients. The area under the ROC curve of the maximal pleural strain value to identify lung sliding abolition was 1.00 [95%CI 1.00; 1.00]. Specificity was 100% [95%CI 93%; 100%] and sensitivity was 100% [95%CI 93%; 100%] with the best cut-off of 4%. Over 104 ultrasound loops, the blinded observer made two errors with B-Mode and none with speckle tracking. The median diagnosis time was 3 [2-5] seconds for B-Mode versus 2 [1-2] seconds for speckle tracking (p = 0.001).

CONCLUSION

Speckle tracking technology allows lung sliding quantification and detection of lung sliding abolition in case of pneumothorax on selected ultrasound loops.