人工智能在先天性心脏病中的作用。

Subhrashis Guha Niyogi, Deb Sanjay Nag, Mandar Mahavir Shah, Amlan Swain, Chandrima Naskar, Preeti Srivastava, Ravi Kant
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引用次数: 0

摘要

这篇综述探讨了人工智能(AI)在改善先天性心脏病(CHDs)的诊断、管理和长期护理方面的变革潜力。从产前筛查到产后管理和长期监测,人工智能在冠心病护理的各个方面都取得了重大进展。使用人工智能算法、增强的胎儿超声心动图和基因检测可以改善产前诊断和风险分层。出生后,人工智能彻底改变了诊断成像分析,提供了更准确和有效的冠心病亚型和严重程度的识别。与传统方法相比,先进的信号处理技术可以更精确地评估血流动力学参数。人工智能驱动的决策支持系统可定制治疗策略,从而优化治疗干预措施,并更准确地预测患者预后。这种个性化的方法可以带来更好的临床结果和降低发病率。此外,支持人工智能的远程监测和可穿戴设备有助于持续监测,从而能够早期发现并发症并及时提供干预措施。这种持续的监测在术后初期和患者的一生中都是至关重要的。尽管人工智能潜力巨大,但挑战依然存在。其中包括对标准化数据集的需求、开发透明和可理解的人工智能算法、伦理考虑以及与现有临床工作流程的无缝集成。通过协作数据共享和负责任的实施来克服这些障碍,将释放人工智能的全部潜力,改善冠心病患者的生活,最终改善患者的治疗效果和生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of artificial intelligence in congenital heart disease.

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.

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