Human versus Artificial Intelligence–Based Echocardiographic Analysis as a Predictor of Outcomes: An Analysis from the World Alliance Societies of Echocardiography COVID Study

Asch, Federico M.; Descamps, Tine; Sarwar, Rizwan; Karagodin, Ilya; Singulane, Cristiane Carvalho; Xie, Mingxing; Tucay, Edwin S.; Rodrigues, Ana Clara Tude; Vasquez-Ortiz, Zuilma Y; Monaghan, Mark; Salazar, Bayardo A Ordonez; Soulat-Dufour, Laurie; Alizadehasl, Azin; Mostafavi, Atoosa; Moreo, Antonella; Citro, Rodolfo; Narang, Akhil; Wu, Chun; Addetia, Karima; Upton, Ross; Woodward, Gary; Lang, Roberto M.

doi:10.1016/j.echo.2022.07.004

Cited by 14 publications

(13 citation statements)

References 36 publications

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“…Automated GLS measurement has been used for GLS analysis in a large multicenter cohort study that investigated Echo characteristics that correlated with in-hospital mortality among patients with COVID-19 [ 13 ]. In the same study cohort (WASE-COVID), automated GLS measurement was validated against manual quantification, and also found to reduce inter-observer variability [ 16 ]. AI-powered software also allows for higher throughput with less manual labor, making routine GLS assessment of Echo images a possibility.…”

Section: Discussionmentioning

confidence: 99%

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Global Longitudinal Strain Is Associated with Mortality in Patients with Multiple Myeloma

Cui

Castagna

Hanif

et al. 2023

JCM

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Patients with multiple myeloma (MM) are at a high risk for developing cardiovascular complications. Global longitudinal strain (GLS) can detect early functional impairment before structural abnormalities develop. It remains unknown if reduced GLS is associated with reduced survival in patients with MM. We conducted a retrospective cohort analysis of patients diagnosed with MM between 1 January 2000 and 31 December 2017 at our institution. Patients with a 2D transthoracic echocardiogram completed within 1 year of MM diagnosis, left ventricular ejection fraction (LVEF) greater than 40%, and no history of myocardial infarction prior to MM diagnosis were included. GLS was measured using an artificial-intelligence-powered software (EchoGo Core), with reduced GLS defined as an absolute value of <18%. The primary outcome of interest was overall survival since myeloma diagnosis. Our cohort included 242 patients with a median follow up of 4.28 years. Fifty-two (21.5%) patients had reduced average GLS. Patients with reduced GLS were more likely to have an IVSd ≥ 1.2cm, E/E’ > 9.6, LVEF/GLS > 4.1, higher LV mass index, and low-voltage ECG. A Total of 126 (52.1%) deaths occurred during follow-up. Overall survival was lower among patients with reduced GLS (adjusted HR: 1.81, CI: 1.07–3.05).

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Section: Discussionmentioning

confidence: 99%

“…However, GLS measurement can be time-consuming and requires additional sonographer and physicians training. Recently, new developments in artificial intelligence (AI) introduced promising results for improved efficiency and reproducibility in GLS measurement [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Global Longitudinal Strain Is Associated with Mortality in Patients with Multiple Myeloma

Cui

Castagna

Hanif

et al. 2023

JCM

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“…However, it is operator dependent and has a non-negligible variability, which may influence the clinician’s therapeutic decisions [ 2 , 3 , 4 , 5 ]. Recent technological developments in artificial intelligence (AI) have enabled automated workflows for view selection and LV function assessment [ 6 , 7 , 8 ]. Still, it is a technique that has not yet found widespread application in the daily clinical practice of Echo.…”

Section: Introductionmentioning

confidence: 99%

“…Still, it is a technique that has not yet found widespread application in the daily clinical practice of Echo. This circumstance is likely due to skepticism, as only very few of these AI-based applications have been tested in a real-life clinical setting [ 6 ]. In addition, unresolved problems, such as the dependence on ultrasound systems or the inability to process analysis results offline on a workstation, may limit its attractiveness for use in clinical routines [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…This circumstance is likely due to skepticism, as only very few of these AI-based applications have been tested in a real-life clinical setting [ 6 ]. In addition, unresolved problems, such as the dependence on ultrasound systems or the inability to process analysis results offline on a workstation, may limit its attractiveness for use in clinical routines [ 6 , 7 ]. Recently, a commercially available and ready-to-use AI-based application for fully automated LV classification and quantification without human interaction (LVivo Seamless TM , DIA Imaging Analysis, Beer-Sheva, Israel) has been developed to address exactly these issues.…”

Section: Introductionmentioning

confidence: 99%

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Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography

et al. 2023

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Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.

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Imaging for Cardiovascular Complications of COVID-19: Cardiac Manifestations in Context

Crosier

Kafil

Paterson

2023

Canadian Journal of Cardiology

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Human versus Artificial Intelligence–Based Echocardiographic Analysis as a Predictor of Outcomes: An Analysis from the World Alliance Societies of Echocardiography COVID Study

Cited by 14 publications

References 36 publications

Global Longitudinal Strain Is Associated with Mortality in Patients with Multiple Myeloma

Global Longitudinal Strain Is Associated with Mortality in Patients with Multiple Myeloma

Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography

Imaging for Cardiovascular Complications of COVID-19: Cardiac Manifestations in Context

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