Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation

Abstract

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 2-chamber 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.

Keywords: ECMO; artificial intelligence; critical monitoring; echocardiography; left ventricular function; pediatrics.

Figure 1

Overview of the PEQ-Net workflow. In the input part, we use two echocardiographic views, A2C and A4C. The two views echocardiographic sequences are segmented and quantified at the same time by a unified model. The segmentation part is divided into three stages: (a) Feature extraction, (b) Feature fusion pyramid, (c) Deep supervision. This part outputs a frame-by-frame segmentation of the echocardiographic sequence. In the quantification part, ED frames and ES frames are obtained based on the detection of the peak area of each frame in the sequence. Then, we use Simpson's biplane method for volume quantification. Further ED and ES volumes and ejection fractions can be obtained for each case.

Figure 2

Quantitative results of PEQ-Net in LV segmentation. The red and green contour lines represent the ground truth and predicted masks in segmentation, respectively.

Figure 3

High performance of the PEQ-Net for segmentation in all frames. The x-axis represents the different views, i.e., A2C, A4C, and A2C + A4C. The y-axis represents each index. From left to right, each column represents the different indices, i.e., Jaccard, Dice, Precision, Recall and HD.

Figure 4

The P-R curve of PEQ-Net in LV segmentation. The red line represents the A2C view. The green line represents the A4C view. The blue line represents both A2C and A4C views. We use the area under the curve to evaluate the performance.

Figure 5

Effectiveness evaluation of PEQ-Net in LV segmentation. The x-axis is the training iteration. The y-axis is the loss function.

Figure 6

High agreement between the detected regions by the PEQ-Net and the region of interest. From left to right, each column indicates the attention map across different training iteration (5,000, 10,000, 20,000, 30,000, 40,000).

Figure 7

Correlations and bland-altman plots between different methods. (Top)Correlation plots and (Bottom)Bland-Altman plots between: automated EF and junior EF (a,d); Expert and junior EF (b,e); automated EF and expert EF (c,f). CI, confidence interval; ICC, intraclass correlation coefficient; EF, sejection fraction.