Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Center of Fetal Medicine, Department of Gynecology, Fertility and Obstetrics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
Ultrasound Obstet Gynecol. 2024 Jul;64(1):36-43. doi: 10.1002/uog.27608. Epub 2024 Jun 3.
Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18-22-week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA.
We 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. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four-chamber and three-vessel views were included in a logistic regression-based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver-operating-characteristics (ROC) curves.
At the 18-22-week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter 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 had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%.
The integration of AI technology with automated cardiac biometric measurements obtained during the 18-22-week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
尽管胎儿医学和先天性心脏病产前诊断取得了显著进展，但约 60%的孤立性主动脉缩窄（CoA）新生儿在出生前未被发现。CoA 的产前检测已被证明对受影响婴儿的生存率有显著影响。为此，人工智能（AI）在胎儿超声中的应用可能代表着一项突破性进展。我们旨在研究在 18-22 周畸形扫描期间使用 AI 进行自动心脏生物测量是否会提高识别有发生 CoA 风险的胎儿的能力。
我们开发了一种能够识别标准心脏平面并进行自动心脏生物测量的 AI 模型。我们的数据来自 2008 年至 2018 年丹麦四个不同地区的妊娠超声图像和结局数据。将产后诊断为 CoA 的病例与健康对照组以 1:100 的比例配对，并在 2 天内按胎龄匹配。纳入四腔心和三血管切面的心脏生物测量值，并纳入基于逻辑回归的预测模型。为了评估其预测能力，我们在接受者操作特征（ROC）曲线上评估了敏感性和特异性。
在 18-22 周扫描时，比较患有产后 CoA（n=73）和健康对照组（n=7300）的病例时，右心室（RV）面积和长度、左心室（LV）直径以及 RV/LV 面积比和主肺动脉/升主动脉直径比的 Z 评分均大于 0.7，差异具有统计学意义。使用逻辑回归和后向特征选择，我们的预测模型的 ROC 曲线下面积为 0.96，特异性为 88.9%，敏感性为 90.4%。
将 AI 技术与 18-22 周畸形扫描期间获得的自动心脏生物测量相结合，有可能大大提高胎儿 CoA 的筛查性能，并随后提高 CoA 的检出率。未来的研究应阐明如何利用 AI 技术辅助先天性心脏异常的筛查和检测，以改善新生儿结局。©2024 作者。超声在妇产科由约翰威立父子公司出版代表国际妇产科超声学会。
