Weiling Chen ¹ Wu Jinhui ² Zhang Zhenxuan ² Zhifan Gao ² Xunyi Chen ^1* Yu Zhang ^1* Luyao Zhou ^1* Zhou Lin ^1* Zijian Tang ^1* Wei Yu ^1* Shumin Fan ^1* Heye Zhang ² Bei Xia ^1*

• ¹ Department of Ultrasonography, Shenzhen Children's Hospital, Shenzhen, China
• ² School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China

Background: Percutaneous extracorporeal membrane oxygenation (ECMO) is administered to pediatric patients with cardiogenic shock or cardiac arrest. The traditional method uses focal echocardiography to complete the left ventricular measurement. However, echocardiographic determination of the ejection fraction (EF) by manual tracing of the endocardial borders is time consuming and operator dependent. The standard visual assessment is also an inherently subjective procedure. Artificial intelligence (AI) based machine learning-enabled image analysis might provide rapid, reproducible measurements of left ventricular volumes and EF for ECMO patients. Objectives: This study aims to evaluate the applicability of AI for monitoring cardiac function based on Echocardiography in patients with ECMO. Materials and Methods: We conducted a retrospective study involving 29 hospitalized patients who received ECMO support between January 2017 and December 2021. Echocardiogram was performed for patients with ECMO, including at pre-ECMO, during cannulation, during ECMO support, during the ECMO wean, and a follow up within 3 months after weaning. EF assessment of all patients was independently evaluated by junior physicians (junior-EF) and experts (expert-EF) using Simpson's biplane method of manual tracing. Additionally, raw data images of apical 2chamber and 4-chamber views were utilized for EF assessment via a Pediatric ECMO Quantification machine learning-enabled AI (automated-EF). Results: There was no statistically significant difference between the automated-EF and expert-EF for all groups (P>0.05). However, the differences between junior-EF and automated-EF and expert-EF were statistically significant (P<0.05). Inter-group correlation coefficients (ICC) indicated higher agreement between automated-EF and expert manual tracking (ICC: 0.983, 95% CI: 0.977~0.987) compared to junior assessments (ICC: 0.932, 95% CI: 0.913~0.946). Bland-Altman analysis showed good agreements among the automated-EF and the expert-EF and junior-EF assessments. There was no significant intra-observer variability for experts' manual tracking or automated measurements. Conclusions: Automated EF measurements are feasible for pediatric ECMO echocardiography. AI-automated analysis of echocardiography for quantifying left ventricular function in critically ill children has good consistency and reproducibility with that of clinical experts. The automated echocardiographic EF method is reliable for the quantitative evaluation of different heart rates. It can fully support the course of ECMO treatment, and it can help improve the accuracy of quantitative evaluation.