Accuracy of left ventricular ejection fraction determined by automated analysis of handheld echocardiograms: A comparison of experienced and novice examiners

Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement

The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

Evaluation of left ventricular ejection fraction by a new automatic tool on a pocket ultrasound device: Concordance study with cardiac magnetic resonance imaging

INTRODUCTION

Assessment of left ventricular ejection fraction (LVEF) is one of the primary objectives of echocardiography. The gold standard assessment technique in emergency medicine is eyeballing. A new tool is now available on pocket ultrasound devices (PUD): automatic LVEF. The primary aim of this study was to evaluate the concordance between LVEF values estimated by automatic LVEF with PUD and by cardiac magnetic resonance imaging (MRI).

MATERIALS

This was a prospective, monocentric, and observational study. All adult patients with an indication for cardiac MRI underwent a point-of-care ultrasound. Blinded to the MRI results, the emergency physician assessed LVEF using the automatic PUD tool and by visual evaluation.

RESULTS

Sixty patients were included and analyzed. Visual estimation of LVEF was feasible for all patients and automatic evaluation for 52 (87%) patients. Lin's concordance correlation coefficient between automatic ejection fraction with PUD and by cardiac MRI was 0.23 (95% CI, 0.03-0.40).

CONCLUSION

Concordance between LVEF estimated by the automatic ejection fraction with PUD and LVEF estimated by MRI was non-existent.

Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views

This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).

Automatic tablet-based monoplane quantification of stroke volume and left ventricular ejection fraction: A comparative assessment against computer-based biplane and monoplane tools

BACKGROUND

Point-of-care cardiovascular left ventricle ejection fraction (LVEF) quantification is established, but automatic tablet-based stroke volume (SV) quantification with handheld ultrasound (HAND) devices is unexplored. We evaluated a tablet-based monoplane LVEF and LV volume quantification tool (AutoEF) against a computer-based tool (Tomtec) for LVEF and SV quantification.

METHODS

Patients underwent HAND scans, and LVEF and SV were quantified using AutoEF and computer-based software that utilized either apical four-chamber views (Auto Strain-monoplane [AS-mono]) or both apical four-chamber and apical two-chamber views (Auto Strain-biplane [AS-bi]). Correlation and Bland-Altman analysis were used to compare AutoEF with AS-mono and AS-bi.

RESULTS

Out of 43 participants, eight were excluded. AutoEF showed a correlation of .83 [.69:.91] with AS-mono for LVEF and .68 [.44:.82] for SV. The correlation with AS-bi was .79 [.62:.89] for LVEF and .66 [.42:.81] for SV. The bias between AutoEF and AS-mono was 4.88% [3.15:6.61] for LVEF and 17.46 mL [12.99:21.92] for SV. The limits of agreement (LOA) were [-5.50:15.26]% for LVEF and [-8.02:42.94] mL for SV. The bias between AutoEF and AS-bi was 6.63% [5.31:7.94] for LVEF and 20.62 mL [16.18:25.05] for SV, with LOA of [-1.20:14.47]% for LVEF and [-4.71:45.94] mL for SV.

CONCLUSION

LVEF quantification with AutoEF software was accurate and reliable, but SV quantification showed limitations, indicating non-interchangeability with neither AS-mono nor AS-bi. Further refinement of AutoEF is needed for reliable SV quantification at the point of care.

Nurse-led home-based detection of cardiac dysfunction by ultrasound: results of the CUMIN pilot study

Aims

Access to echocardiography is a significant barrier to heart failure (HF) care in many low- and middle-income countries. In this study, we hypothesized that an artificial intelligence (AI)-enhanced point-of-care ultrasound (POCUS) device could enable the detection of cardiac dysfunction by nurses in Tunisia.

Methods and results

This CUMIN study was a prospective feasibility pilot assessing the diagnostic accuracy of home-based AI-POCUS for HF conducted by novice nurses compared with conventional clinic-based transthoracic echocardiography (TTE). Seven nurses underwent a one-day training program in AI-POCUS. A total of 94 patients without a previous HF diagnosis received home-based AI-POCUS, POC N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing, and clinic-based TTE. The primary outcome was the sensitivity of AI-POCUS in detecting a left ventricular ejection fraction (LVEF) <50% or left atrial volume index (LAVI) >34 mL/m2, using clinic-based TTE as the reference. Out of seven nurses, five achieved a minimum standard to participate in the study. Out of the 94 patients (60% women, median age 67), 16 (17%) had an LVEF < 50% or LAVI > 34 mL/m2. AI-POCUS provided an interpretable LVEF in 75 (80%) patients and LAVI in 64 (68%). The only significant predictor of an interpretable LVEF or LAVI proportion was the nurse operator. The sensitivity for the primary outcome was 92% [95% confidence interval (CI): 62-99] for AI-POCUS compared with 87% (95% CI: 60-98) for NT-proBNP > 125 pg/mL, with AI-POCUS having a significantly higher area under the curve (P = 0.040).

Conclusion

The study demonstrated the feasibility of novice nurse-led home-based detection of cardiac dysfunction using AI-POCUS in HF patients, which could alleviate the burden on under-resourced healthcare systems.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Clinical comparison of a handheld cardiac ultrasound device for the assessment of left ventricular function

PURPOSE

Recently developed handheld ultrasound devices (HHUD) represent a promising method to evaluate the cardiovascular abnormalities at the point of care. However, this technology has not been rigorously evaluated. The aim of this study was to explore the correlation and the agreement between the LVEF (Left Ventricular Ejection Fraction) visually assessed by a moderately experienced sonographer using an HHUD compared to the routine LVEF assessment performed at the Echocardiography Laboratory.

METHODS

This was a prospective single center study which enrolled 120 adult inpatients and outpatients referred for a comprehensive Echocardiography (EC).

RESULTS

The mean age of the patients was 69.9 ± 12.5 years. There were 47 females (39.2%). The R-squared was r 0.94 (p < 0.0001) and the ICC was 0.93 (IC 95% 0.91-0.95, p ≤ 0.0001). The Bland-Altman plot showed limits of agreement (LOA): Upper LOA 10.61 and Lower LOA - 8.95. The overall agreement on the LVEF assessment when it was stratified as "normal" or "reduced" was 89.1%, with a kappa of 0.77 (p < 0.0001). When the LVEF was classified as "normal", "mildly reduced", "moderately reduced", or "severely reduced," the kappa was 0.77 (p < 0.0001). The kappa between the HHUD EC and the comprehensive EC for the detection of RWMAs in the territories supplied by the LAD, LCX and RCA was 0.85, 0.73 and 0.85, respectively.

CONCLUSION

With current HHUD, an averagely experienced operator can accurately bedside visual estimate the LVEF. This may facilitate the incorporation of this technology in daily clinical practice improving the management of patients.

A cost-effectiveness analysis of empagliflozin for heart failure patients across the full spectrum of ejection fraction in Spain: combined results of the EMPEROR-Preserved and EMPEROR-Reduced trials

BACKGROUND

Heart failure (HF) is a chronic condition with considerable clinical burden for patients and economic burden for healthcare systems. Treatment for HF is typically based on ejection fraction (EF) phenotype. The cost-effectiveness of empagliflozin + standard of care (SoC) compared to SoC has been examined for HF phenotypes below or above 40% EF separately, but not across the full spectrum of EF in Spain.

METHODS

The results of two preexisting, validated, and published phenotype-specific Markov cohort models were combined using a population-weighted approach, reflecting the incidence of each phenotype in the total HF population in Spain. A probabilistic sensitivity analysis was performed by sampling each model's probabilistic results.

RESULTS

Empagliflozin + SoC compared to SoC resulted in increased life-years (LYs) (6.48 vs. 6.35), quality-adjusted LYs (QALYs) (4.80 vs. 4.63), and healthcare costs (€19,090 vs. €18,246), over a lifetime time horizon for the combined HF population in Spain. The incremental cost-effectiveness ratio (ICER) was €5,089/QALY. All subgroup, scenario, and probabilistic ICERs were consistently below €10,000/QALY.

CONCLUSIONS

Empagliflozin is the first treatment with established efficacy and cost-effectiveness for HF patients across EF from the perspective of healthcare payers in Spain. Empagliflozin also proved to be cost-effective for all subgroups of patients included in the analysis.

The importance of patient characteristics, operators, and image quality for the accuracy of heart failure diagnosis by general practitioners using handheld ultrasound devices

Aims

To evaluate whether the characteristics of patients, operators, and image quality could explain the accuracy of heart failure (HF) diagnostics by general practitioners (GPs) using handheld ultrasound devices (HUDs) with automatic decision-support software and telemedical support.

Methods and results

Patients referred to an outpatient cardiac clinic due to symptoms indicating HF were examined by one of five GPs after dedicated training. In total, 166 patients were included [median (inter-quartile range) age 73 (63-78) years; mean ± standard deviation ejection fraction 53 ± 10%]. The GPs considered whether the patients had HF in four diagnostic steps: (i) clinical examination, (ii) adding focused cardiac HUD examination, (iii) adding automatic decision-support software measuring mitral annular plane systolic excursion (autoMAPSE) and ejection fraction (autoEF), and (iv) adding telemedical support. Overall, the characteristics of patients, operators, and image quality explained little of the diagnostic accuracy. Except for atrial fibrillation [lower accuracy for HUD alone and after adding autoEF (P < 0.05)], no patient characteristics influenced the accuracy. Some differences between operators were found after adding autoMAPSE (P < 0.05). Acquisition errors of the four-chamber view and a poor visualization of the mitral plane were associated with reduced accuracy after telemedical support (P < 0.05).

Conclusion

The characteristics of patients, operators, and image quality explained just minor parts of the modest accuracy of GPs' HF diagnostics using HUDs with and without decision-support software. Atrial fibrillation and not well-standardized recordings challenged the diagnostic accuracy. However, the accuracy was only modest in well-recorded images, indicating a need for refinement of the technology.

Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction

Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.

Cost-effectiveness of empagliflozin in heart failure patients irrespective of ejection fraction in England

AIMS

Heart failure (HF) is a complex syndrome commonly categorized into two main phenotypes [left ventricular ejection fraction (LVEF) below or above 40%], and although empagliflozin is the first approved medication with proven clinical effectiveness for both phenotypes, its cost-effectiveness of treating the entire HF population remains unknown.

METHODS

The analysis was performed utilizing two preexisting, LVEF phenotype-specific cost-effectiveness models to estimate the cost-effectiveness of empagliflozin in adults for the treatment of symptomatic chronic HF, irrespective of ejection fraction (EF). The results of the phenotype-specific models were combined using a population-weighted approach to estimate the deterministic and probabilistic incremental cost-effectiveness ratios (ICERs).

RESULTS

Based on combined results, empagliflozin + standard of care (SoC) is associated with 6.13 life-years (LYs) and 3.92 quality-adjusted life-years (QALYs) compared with 5.98 LYs and 3.76 QALYs for SoC alone over a lifetime, resulting in an incremental difference of 0.15 LYs and 0.16 QALYs, respectively. Total lifetime healthcare costs per patient are £15 246 for empagliflozin + SoC and £13 982 for SoC giving an incremental difference of £1264. The ICER is £7757/QALY, which is substantially lower than the willingness-to-pay (WTP) of £30 000 per QALY used by NICE. The results of the probabilistic sensitivity analyses are in line with the deterministic results.

CONCLUSION

Empagliflozin is the first efficacious, approved, and cost-effective treatment option for all HF patients, irrespective of EF. The combined ICER was consistently below the WTP threshold. Therefore, empagliflozin offers value for money for the treatment of the full HF population in England.

[Ultrasound systems for abdominal diagnostics - current methods, clinical applications and new technologies]

Abdominal ultrasound is the method of first choice in many clinical situations. Gray scale imaging (B-mode) and conventional Doppler techniques are nowadays complemented by contrast-enhanced ultrasound (CEUS), elastography, fat quantification and further technologies which allow multimodal characterization of organs and tissue structure using panoramic imaging, 3D-techniques and image fusion. The development of small portable devices augments the spectrum for sonographic diagnostics. In this review, we describe the current status of ultrasound technology based on published evidence. In addition, we provide guidance for quality assurance.

Clinical Influence of Handheld Ultrasound, Supported by Automatic Quantification and Telemedicine, in Suspected Heart Failure

Early and correct heart failure (HF) diagnosis is essential to improvement of patient care. We aimed to evaluate the clinical influence of handheld ultrasound device (HUD) examinations by general practitioners (GPs) in patients with suspected HF with or without the use of automatic measurement of left ventricular (LV) ejection fraction (autoEF), mitral annular plane systolic excursion (autoMAPSE) and telemedical support. Five GPs with limited ultrasound experience examined 166 patients with suspected HF (median interquartile range = 70 (63-78) y; mean ± SD EF = 53 ± 10%). They first performed a clinical examination. Second, they added an examination with HUD, automatic quantification tools and, finally, telemedical support by an external cardiologist. At all stages, the GPs considered whether the patients had HF. The final diagnosis was made by one of five cardiologists using medical history and clinical evaluation including a standard echocardiography. Compared with the cardiologists' decision, the GPs correctly classified 54% by clinical evaluation. The proportion increased to 71% after adding HUDs, and to 74 % after telemedical evaluation. Net reclassification improvement was highest for HUD with telemedicine. There was no significant benefit of the automatic tools (p ≥ 0.58). Addition of HUD and telemedicine improved the GPs' diagnostic precision in suspected HF. Automatic LV quantification added no benefit. Refined algorithms and more training may be needed before inexperienced users benefit from automatic quantification of cardiac function by HUDs.

Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm

OBJECTIVES

A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical 4-chamber (A4C) images.

METHODS

Researchers used a long-short-term-memory algorithm for image analysis. Using the Stanford EchoNet-Dynamic database of 10,036 A4C videos with calculated exact EF, researchers tested 3 ML training permeations. First, training on unaltered Stanford A4C videos, then unaltered and 90° clockwise (CW) rotated videos and finally unaltered, 90° rotated and horizontally flipped videos. As a real-world test, we obtained 615 SX videos from Harbor-UCLA (HUCLA) with EF calculations in 5% ranges. Researchers performed 1000 randomizations of EF point estimation within HUCLA EF ranges to compensate for ML and HUCLA EF mismatch, obtaining a mean value for absolute error (MAE) comparison and performed Bland-Altman analyses.

RESULTS

The ML algorithm EF mean MAE was estimated at 23.0, with a range of 22.8-23.3 using unaltered A4C video, mean MAE was 16.7, with a range of 16.5-16.9 using unaltered and 90° CW rotated video, mean MAE was 16.6, with a range of 16.3-16.8 using unaltered, 90° CW rotated and horizontally flipped video training. Bland-Altman showed weakest agreement at 40-45% EF.

CONCLUSIONS

Researchers successfully adapted unrelated ultrasound window data to train a POCUS ML algorithm with fair MAE using data manipulation to simulate a different ultrasound examination. This may be important for future POCUS algorithm design to help overcome a paucity of POCUS databases.

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

BACKGROUND

Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.

AIM

To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.

METHODS

A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.

RESULTS

The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.

CONCLUSION

This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.

Assessment and validation of a novel fast fully automated artificial intelligence left ventricular ejection fraction quantification software

BACKGROUND

Quantification of left ventricular ejection fraction (LVEF) by transthoracic echocardiography (TTE) is operator-dependent, time-consuming, and error-prone. LVivoEF by DIA is a new artificial intelligence (AI) software, which displays the tracking of endocardial borders and rapidly quantifies LVEF. We sought to assess the accuracy of LVivoEF compared to cardiac magnetic resonance imaging (cMRI) as the reference standard and to compare LVivoEF to the standard-of-care physician-measured LVEF (MD-EF) including studies with ultrasound enhancing agents (UEAs).

METHODS

In 273 consecutive patients, we compared MD-EF and AI-derived LVEF to cMRI. AI-derived LVEF was obtained from a non-UEA four-chamber view without manual correction. Thirty-one patients were excluded: 25 had interval interventions or incomplete TTE or cMRI studies and six had uninterpretable non-UEA apical views.

RESULTS

In the 242 subjects, the correlation between AI and cMRI was r = .890, similar to MD-EF and cMRI with r = .891 (p = 0.48). Of the 126 studies performed with UEAs, the correlation of AI using the unenhanced four-chamber view was r = .89, similar to MD-EF with r = .90. In the 116 unenhanced studies, AI correlation was r = .87, similar to MD-EF with r = .84. From Bland-Altman analysis, LVivoEF underreported the LVEF with a bias of 3.63 ± 7.40% EF points compared to cMRI while MD-EF to cMRI had a bias of .33 ± 7.52% (p = 0.80).

CONCLUSIONS

Compared to cMRI, LVivoEF can accurately quantify LVEF from a standard apical four-chamber view without manual correction. Thus, LVivoEF has the ability to improve and expedite LVEF quantification.

Focal cardiac ultrasound learning with pocked ultrasound device: A bicentric prospective blinded randomized study

PURPOSE

Point-of-care ultrasound using a pocket-ultrasound-device (PUD) is increasing in clinical medicine but the optimal way to teach focused cardiac ultrasound is not clear. We evaluated whether teaching using a PUD or a conventional-ultrasound-device (CUD) is different when the final exam was conducted on a PUD. The primary aim was to compare the weighted total quality scale (WTQS, out of 100) obtained by participants in the two groups (CUD and PUD) on a live volunteer 2-4 weeks after their initial training. The secondary aims were to compare examination time and students' confidence levels (out of 50).

METHODS

This bicentric, prospective single-blind randomized trial included undergraduate medical students. After watching a 15 min video about echocardiography views, students had a 45 min hands-on training session with a live volunteer using a PUD or a CUD. The final examination was conducted with a PUD on a live volunteer.

RESULTS

Eighty-six comparable students were included, with 4 ± 1 years of medical training. In the PUD group, the mean WTQS was 65 ± 16 versus 60 ± 15 in the CUD group [p = 0.22; in multivariate analysis, OR 0.8 95% CI (0.1;1.6), p = 0.34]. The examination time was 10.0 [6.2-12.4] min in the PUD group versus 11.4 [7.3-13.2] in the CUD group (p = 0.39), while the confidence level was 27.9 ± 7.7 in the PUD group versus 27.4 ± 7.2 in the CUD group (p = 0.76).

CONCLUSION

There was no difference between teaching echocardiographic views using a PUD as compared to a CUD on the PUD image quality, exam time, or confidence level of students.

Diagnostic accuracy of handheld cardiac ultrasound device for assessment of left ventricular structure and function: systematic review and meta-analysis

Objective

Handheld ultrasound devices (HUD) has diagnostic value in the assessment of patients with suspected left ventricular (LV) dysfunction. This meta-analysis evaluates the diagnostic ability of HUD compared with transthoracic echocardiography (TTE) and assesses the importance of operator experience.

Methods

MEDLINE and EMBASE databases were searched in October 2020. Diagnostic studies using HUD and TTE imaging to determine LV dysfunction were included. Pooled sensitivities and specificities, and summary receiver operating characteristic curves were used to determine the diagnostic ability of HUD and evaluate the impact of operator experience on test accuracy.

Results

Thirty-three studies with 6062 participants were included in the meta-analysis. Experienced operators could predict reduced LV ejection fraction (LVEF), wall motion abnormality (WMA), LV dilatation and LV hypertrophy with pooled sensitivities of 88%, 85%, 89% and 85%, respectively, and pooled specificities of 96%, 95%, 98% and 91%, respectively. Non-experienced operators are able to detect cardiac abnormalities with reasonable sensitivity and specificity. There was a significant difference in the diagnostic accuracy between experienced and inexperienced users in LV dilatation, LVEF (moderate/severe) and WMA. The diagnostic OR for LVEF (moderate/severe), LV dilatation and WMA in an experienced hand was 276 (95% CI 58 to 1320), 225 (95% CI 87 to 578) and 90 (95% CI 31 to 265), respectively, compared with 41 (95% CI 18 to 94), 45 (95% CI 16 to 123) and 28 (95% CI 20 to 41), respectively, for inexperienced users.

Conclusion

This meta-analysis is the first to establish HUD as a powerful modality for predicting LV size and function. Experienced operators are able to accurately diagnose cardiac disease using HUD. A cautious, supervised approach should be implemented when imaging is performed by inexperienced users. This study provides a strong rationale for considering HUD as an auxiliary tool to physical examination in secondary care, to aid clinical decision making when considering referral for TTE.

Trial registration number

CRD42020182429.

Cardiovascular examination using hand-held cardiac ultrasound

Echocardiography is the first-line imaging modality for assessing cardiac function and morphology. The miniaturisation of ultrasound technology has led to the development of hand-held cardiac ultrasound (HCU) devices. The increasing sophistication of available HCU devices enables clinicians to more comprehensively examine patients at the bedside. HCU can augment clinical exam findings by offering a rapid screening assessment of cardiac dysfunction in both the Emergency Department and in cardiology clinics. Possible implications of implementing HCU into clinical practice are discussed in this review paper.