Case Study

Use of Artificial Intelligence for Real-time, Automatic Quantification of Left Ventricular Ejection Fraction by a Novel Handheld Ultrasound Device

Vasileios Sachpekidis, Stella-Lida Papadopoulou, Vasiliki Kantartzi, Ioannis Styliadis, Petros Nihoyannopoulos

Background

Transthoracic echocardiography is the most widely used imaging modality for the assessment of left ventricular ejection fraction (LVEF), which is crucial for patient care and management. Most recently, artificial intelligence (AI) has been employed to automatically identify LV endocardial boundaries and calculate LVEF automatically. Technological advancements have enabled the development of small handheld ultrasound devices (HUDs) that can provide echocardiographic images at the point of care; however, until now the HUDs have presented limited quantification capabilities. The application of AI algorithms to point-of-care echocardiography may facilitate the acquisition and accurate interpretation of a high volume of imaging data in real-time.

Purpose

To evaluate the accuracy of a novel HUD with AI-assisted algorithm (auto-EF) to automatically calculate LVEF.

Method

We prospectively included consecutive patients who were referred to a tertiary hospital echocardiography laboratory for a standard echocardiography examination. The modified biplane Simpson’s method was used to determine LV volumes and function from the apical four-chamber and apical two-chamber views. All patients were subsequently scanned with the Kosmos HUD and a fully automated estimation of the LVEF was possible after acquisition of the same two apical views within seconds by the device itself with the use of the AI-assisted auto-EF algorithm. The image quality for each examination was assessed and classified as good, moderate and poor. The auto-EF measurements were compared head-to-head with the manually traced biplane Simpson’s rule measurements on cart-based systems as the reference standard, using linear regression and Bland-Altman analysis.

Results

The study comprised 100 consecutive patients (57±15 years old, 61% male), including 38 patients with abnormal LVEF<50%. The image quality for the cart-based systems acquisition was assessed as good in 45%, moderate in 50% and poor in 5% of patients and for the handheld ultrasound device acquisition as good in 31%, moderate in 57% and poor in 12% of patients.

Auto-EF measurement by AI-assisted algorithm was feasible in all patients with LVEF measurements on cart-based systems. There was good agreement between the reference standard and Kosmos auto-EF algorithm with a correlation coefficient r=0.87, 95%CI 0.81-0.91, (p<0.0001). The corresponding Bland-Altman plot showed a small non-significant bias of -1.42% (p=0.058), with limits of agreement ±14.5% for the auto-EF. The paired comparison of the LVEF calculation by the 2 methods did not reveal a significant difference between reference standard and HUD auto-EF [56% (IQR 40% - 62%) vs 53% (IQR 43% - 59%) respectively, p=0.106].

Bland-Altman plot.

Conclusion

The AI-assisted auto-EF algorithm in a novel handheld ultrasound device can accurately calculate LVEF in real-time as compared to the recommended manual biplane Simpson’s method on cart-based systems in an “all-comers” patient population.

Correlation of the LVEF measurements.

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