Role of AI-assisted automated cardiac biometrics in screening for fetal coarctation of aorta

OBJECTIVES

Although there have been remarkable strides in fetal medicine and prenatal diagnosis of congenital heart disease, a significant percentage of newborns with isolated coarctation of the aorta (CoA) - around 60 percent - are still not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on the survival rates of affected infants. To this end, the implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advancement. Our hypothesis is that leveraging automated cardiac biometric measurements with AI during the 18-22-week anomaly scan will enhance the identification of fetuses that are at risk of developing CoA.

METHODS

We have developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. The CoA cases from the period were paired with healthy controls in a ratio of 1:100 and matched on gestational ages of ±2 days. The cardiac biometrics on the four-chamber view and three vessel view were included in a logistic regression-based prediction model. To assess the predictive capabilities, we visualized sensitivity and specificity on Receiver Operating Characteristic (ROC) curves.

RESULTS

At the 18-22 week scan, the right ventricle (RV)area and length, left ventricle (LV) width, and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences with z-scores above 0.7 when comparing subjects with a postnatal diagnosis of CoA (n=73) and healthy controls (n=7300). Using logistic regression and backward feature selection, our prediction model produced a ROC curve with an AUC (Area Under the Curve) of 0.96 and a specificity of 88.9% at a sensitivity level of 90.4%.

CONCLUSION

The integration of AI technology with automated cardiac biometric measurements conducted during the 18-22-week anomaly scan in fetal medicine has the potential to substantially enhance the screening for fetal CoA and subsequently the rate of CoA detection. Future research should clarify how AI technology can be used to aid in screening and detection of congenital heart anomalies to improve neonatal outcomes. This article is protected by copyright. All rights reserved.

Prevalence and detection rate of major congenital heart disease in twin pregnancies in Denmark

OBJECTIVE

To investigate the national prevalence and prenatal detection rate (DR) of major congenital heart disease (mCHD) in twin pregnancies without twin-to-twin transfusion syndrome (TTTS)-associated CHD in a Danish population following a standardized prenatal screening program.

METHODS

This was a national registry-based study of data collected prospectively over a 10-year period. In Denmark, all women with a twin pregnancy are offered standardized screening and surveillance programs in addition to first- and second-trimester screening for aneuploidies and malformation, respectively: monochorionic (MC) twins every 2 weeks from gestational week 15 and dichorionic (DC) twins every 4 weeks from week 18. The data were retrieved from the Danish Fetal Medicine Database and included all twin pregnancies from 2009-2018, in which at least one fetus had a pre- and/or postnatal mCHD diagnosis. mCHD was defined as CHD requiring surgery within the first year of life, excluding ventricular septal defects. All pregnancy data were pre- and postnatally validated in the local patient files at the four tertiary centers covering the entire country.

RESULTS

A total of 60 cases from 59 twin pregnancies were included. The prevalence of mCHD was 4.6 (95% CI, 3.5-6.0) per 1000 twin pregnancies (1.9 (95% CI, 1.3-2.5) per 1000 live births). The prevalences for DC and MC were 3.6 (95% CI, 2.6-5.0) and 9.2 (95% CI, 5.8-13.7) per 1000 twin pregnancies, respectively. The national prenatal DR of mCHD in twin pregnancies for the entire period was 68.3%. The highest DRs were in cases with univentricular hearts (100%) and the lowest with aortopulmonary window, total anomalous pulmonary venous return, Ebstein's anomaly, aortic valve stenosis and coarctation of the aorta (0-25%). Mothers of children with prenatally undetected mCHD had a significantly higher body mass index (BMI) compared to mothers of children with a prenatally detected mCHD (median, 27 kg/m2 and 23 kg/m2 , respectively; P = 0.02).

CONCLUSIONS

The prevalence of mCHD in twins was 4.6 per 1000 pregnancies and was higher in MC than DC pregnancies. The prenatal DR of mCHD in twin pregnancies was 68.3%. Maternal BMI was higher in cases of prenatally undetected mCHD. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.