Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
J Am Soc Echocardiogr. 2024 May;37(5):550-561. doi: 10.1016/j.echo.2023.12.013. Epub 2024 Jan 9.
Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.
先天性心脏病是新生儿的严重健康风险。在怀孕期间早期检测胎儿心脏结构和功能的异常,可以帮助患者及时寻求诊断和治疗建议,早期干预计划可以显著提高胎儿的存活率。超声心动图是诊断胎儿先天性心脏病最常用和广泛应用的诊断工具之一。然而,由于胎儿、母亲和超声设备等因素的限制,传统的胎儿超声心动图具有一定的局限性,并且高度依赖操作人员的技术水平。人工智能(AI)技术利用先进的计算机算法快速发展,具有为超声医师提供省时、准确诊断的巨大潜力,并弥合不同地区之间的技能差距。近年来,人工智能辅助胎儿超声心动图已成功应用于广泛的超声诊断。本文系统地回顾了近年来人工智能在胎儿超声心动图领域的应用,包括图像处理、生物识别和疾病诊断,并对未来的研究进行了展望。