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人工智能在先天性心脏病中的作用。

Role of artificial intelligence in congenital heart disease.

作者信息

Niyogi Subhrashis Guha, Nag Deb Sanjay, Shah Mandar Mahavir, Swain Amlan, Naskar Chandrima, Srivastava Preeti, Kant Ravi

机构信息

Department of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.

Department of Cardiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India.

出版信息

World J Clin Pediatr. 2025 Sep 9;14(3):105926. doi: 10.5409/wjcp.v14.i3.105926.

Abstract

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient's life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

摘要

本综述探讨了人工智能(AI)在改善先天性心脏病(CHD)诊断、管理及长期护理方面的变革潜力。从产前筛查到产后管理及长期监测,AI在CHD护理的各个环节都带来了重大进展。利用AI算法、增强型胎儿超声心动图和基因检测可改善产前诊断和风险分层。产后,AI彻底改变了诊断成像分析,能更准确、高效地识别CHD亚型及严重程度。与传统方法相比,先进的信号处理技术能更精确地评估血流动力学参数。AI驱动的决策支持系统可定制治疗策略,从而优化治疗干预并更准确地预测患者预后。这种个性化方法能带来更好的临床结果并降低发病率。此外,具备AI功能的远程监测和可穿戴设备便于持续监测,从而能够早期发现并发症并及时进行干预。这种持续监测在术后即刻及患者整个生命过程中都至关重要。尽管AI潜力巨大,但挑战依然存在。这些挑战包括需要标准化数据集、开发透明且易懂的AI算法、伦理考量以及无缝融入现有临床工作流程。通过协作数据共享和负责任的实施来克服这些障碍,将释放AI的全部潜力,改善CHD患者的生活,最终带来更好的患者预后和更高的生活质量。

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5
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