Suha Khadiza Tun, Lubenow Hugh, Soria-Zurita Stefania, Haw Marcus, Vettukattil Joseph, Jiang Jingfeng
Biomedical Engineering Department, Michigan Technological University, Houghton, MI 49931, USA.
Betz Congenital Heart Center, Helen DeVos Children's Hospital, Grand Rapids, MI 49503, USA.
Medicina (Kaunas). 2025 Mar 21;61(4):561. doi: 10.3390/medicina61040561.
Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an array of clinical applications, including image quality control, cardiac function measurements, defect detection, and classifications. Collectively, we answer how integrating AI technologies and echocardiography can help improve the detection of congenital heart defects. Particularly, the superior sensitivity of AI-based congenital heart defect (CHD) detection in the fetus (>90%) allows it to be potentially translated into the clinical workflow as an effective screening tool in an obstetric setting. However, the current AI technologies still have many limitations, and more technological developments are required to enable these AI technologies to reach their full potential. Also, integrating diagnostic AI technologies into the clinical workflow should resolve ethical concerns. Otherwise, deploying diagnostic AI may not address low-resource populations' healthcare access disadvantages. Instead, it will further exacerbate the access disparities. We envision that, through the combination of tele-echocardiography and AI, low-resource medical facilities may gain access to the effective detection of CHD at the prenatal stage.
人工智能（AI）在放射学和心脏病学领域正迅速受到关注，用于准确诊断结构性心脏病。在这篇综述论文中，我们首先概述人工智能和超声心动图的技术背景，然后介绍一系列临床应用，包括图像质量控制、心脏功能测量、缺陷检测和分类。总体而言，我们回答了整合人工智能技术和超声心动图如何有助于改善先天性心脏病缺陷的检测。特别是，基于人工智能的胎儿先天性心脏病（CHD）检测具有较高的灵敏度（>90%），这使其有可能作为产科环境中的一种有效筛查工具纳入临床工作流程。然而，当前的人工智能技术仍有许多局限性，需要更多的技术发展才能使这些人工智能技术发挥其全部潜力。此外，将诊断性人工智能技术整合到临床工作流程中应解决伦理问题。否则，部署诊断性人工智能可能无法解决资源匮乏人群在医疗保健获取方面的劣势。相反，这将进一步加剧获取差距。我们设想，通过远程超声心动图和人工智能的结合，资源匮乏的医疗设施可能能够在产前阶段有效检测先天性心脏病。
