Background Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. Methods 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors. Results The network’s Dice score (ML vs GT) was 0.945 (interquartile range: 0.929–0.955) for the aorta and 0.885 (0.851–0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5–15.7%) and 4.1% (3.1–6.9%), respectively, and for the pulmonary arteries 14.6% (11.5–23.2%) and 6.3% (4.3–7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2). Conclusions ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

Background: Management of hypoplastic left heart syndrome (HLHS) presents many challenges. We describe our institutional outcomes for management of patients with HLHS over the past 12 years and highlight our strategy for those with highly restrictive/intact interatrial septum (R/I-IAS). Methods: Eighty-eight neonates with HLHS underwent surgical treatment, divided equally into Era-I (n = 44, April 2006 to February 2013) and Era-II (n = 44, March 2013 to June 2018). Up to 2013, all patients with R/I-IAS were delivered at an adjacent adult hospital and then moved to our hospital for intensive care and management. From 2014, these patients were delivered at a co-located theatre in our hospital with immediate atrial septectomy. The hybrid approach was occasionally used with preference for the Norwood procedure for suitable candidates. Results: One-year survival after Norwood procedure was 62.5% and 80% for Era-I and Era-II ( P = not significant (ns)), respectively, and 41% of patients were categorized as high risk using conventional criteria. Survival at 1 year differed significantly between high-risk and standard-risk patients ( P = 0.01). For high-risk patients, survival increased from 42% to 65% between eras ( P = ns). In the R/I-IAS subgroup (n = 15), 11 underwent Norwood procedure after emergency atrial septectomy. Of these, seven born at the adjacent adult hospital had 40% survival to stage II versus 60% for the four born at the colocated theatre. Delivery in a colocated theatre reduced the birth-to-cardiopulmonary bypass median time from 445 (150-660) to 62 (52-71) minutes. Conclusion: Reported surgical outcomes are comparable to multicenter reports and international databases. Proactive management for risk factors such as R/I-IAS may contribute to improved overall outcomes.

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