Performance of an automated ultrasound device in identifying and tracing the heart in porcine cardiac arrest

Abstract

Abstract Background While intra-arrest echocardiography can be used to guide and monitor chest compression quality, it is not currently feasible on the scene of out-of-hospital cardiac arrests. Rapid and automated sonographic localization of the heart may provide first-responders guidance to an optimal area of compression without requiring them to interpret ultrasound images. In this proof-of-concept porcine study, we sought to describe the performance of an automated ultrasound device in correctly identifying and tracing the borders of the heart in three distinct states: pre-arrest, arrest, and late arrest. Methods An automated ultrasound device (bladder scanner) was placed on the chests of 7 swine, along the left sternal border (4th–8th intercostal spaces). Scanner-generated images were recorded for each space during pre-arrest, arrest, and finally late arrest. 828 images of the LV and LV outflow tract were randomized and 150 (50/state) selected for analysis. Scanner tracings of the heart were then digitally obscured to facilitate tracing by expert reviewers who were blinded to the physiologic state. Reviewer tracings were compared to bladder scanner tracings; with concordance between these images determined via Sørensen–Dice index (SDI). Results When compared to human reviewers, the bladder scanner was able to identify and trace the borders during cardiac arrest. The bladder scanner performed best at the time of arrest (SDI 0.900 ± 0.059). As resuscitation efforts continued and time from initial arrest increased, the scanner’s performance decreased dramatically (SDI 0.597 ± 0.241 in late arrest). Conclusion An automated ultrasound device (bladder scanner) reliably traced porcine hearts during cardiac arrest. It is possible a device could be developed to indicate where compressions should be performed without requiring the operator to interpret ultrasound images. Further investigation into rapid, automated, sonographic localization of the heart to identify the area of compression in out-of-hospital cardiac arrest is warranted.

Keywords

• Cardiopulmonary resuscitation
• Echocardiography
• Artificial intelligence
• Automation
• Image segmentation
• Cardiac arrest