人工智能辅助胎儿先天性心脏病超声成像：范围综述。

IF 2.8 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-09-16 DOI:10.1016/j.radi.2025.103170

L. Norris, P. Lockwood

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引用次数: 0

摘要

目的：先天性心脏病（CHD）是新生儿死亡和发病的重要原因。在怀孕期间发现这些异常可提高生存率，改善预后，并改善妊娠管理和受影响家庭的生活质量。胎儿超声心动图可以被认为是检测冠心病的准确方法。然而，冠心病的检测可能受到诸如超声医师技能、专业知识和患者特定变量等因素的限制。使用人工智能（AI）有可能解决这些挑战，增加产前护理期间的产前冠心病检测。使用谷歌Scholar、PubMed和ScienceDirect数据库进行范围审查，采用关键词、布尔运算符、纳入和排除标准来确定同行评议的研究。对已发现的文献进行专题制图和综合，以回顾关键概念、研究方法和研究结果。主要发现：共纳入n = 233篇文献，经排除标准后，纳入范围缩小至符合纳入标准的n = 7篇。文献中的主题确定了人工智能在帮助临床医生和受训者方面的潜力，以及超声成像中出现的新的伦理限制。结论：基于人工智能的超声成像工具在辅助超声医师和医生诊断冠心病方面具有很大的潜力。然而，由于数据缺乏和样本量小，需要进一步的研究和技术进步来提高可靠性并将人工智能融入常规临床实践。实践意义：本综述确定了基于人工智能的工具在胎儿心脏超声成像中的准确性和局限性。人工智能有可能帮助减少漏诊、加强培训和改善妊娠管理。有必要了解和解决涉及这种成像新范式的伦理和法律考虑。

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Artificial intelligence aided ultrasound imaging of foetal congenital heart disease: A scoping review

Objectives

Congenital heart diseases (CHD) are a significant cause of neonatal mortality and morbidity. Detecting these abnormalities during pregnancy increases survival rates, enhances prognosis, and improves pregnancy management and quality of life for the affected families. Foetal echocardiography can be considered an accurate method for detecting CHDs. However, the detection of CHDs can be limited by factors such as the sonographer's skill, expertise and patient specific variables. Using artificial intelligence (AI) has the potential to address these challenges, increasing antenatal CHD detection during prenatal care.

A scoping review was conducted using Google Scholar, PubMed, and ScienceDirect databases, employing keywords, Boolean operators, and inclusion and exclusion criteria to identify peer-reviewed studies. Thematic mapping and synthesis of the found literature were conducted to review key concepts, research methods and findings.

Key findings

A total of n = 233 articles were identified, after exclusion criteria, the focus was narrowed to n = 7 that met the inclusion criteria. Themes in the literature identified the potential of AI to assist clinicians and trainees, alongside emerging new ethical limitations in ultrasound imaging.

Conclusion

AI-based tools in ultrasound imaging offer great potential in assisting sonographers and doctors with decision-making in CHD diagnosis. However, due to the paucity of data and small sample sizes, further research and technological advancements are needed to improve reliability and integrate AI into routine clinical practice.

Implications for practice

This scoping review identified the reported accuracy and limitations of AI-based tools within foetal cardiac ultrasound imaging. AI has the potential to aid in reducing missed diagnoses, enhance training, and improve pregnancy management. There is a need to understand and address the ethical and legal considerations involved with this new paradigm in imaging.

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.