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本文引用的文献

1
ACC/AHA and ESC/EACTS Guidelines for the Management of Valvular Heart Diseases: JACC Guideline Comparison.美国心脏病学会/美国心脏协会与欧洲心脏病学会/欧洲心胸外科学会瓣膜性心脏病管理指南：JACC 指南比较。
J Am Coll Cardiol. 2023 Aug 22;82(8):721-734. doi: 10.1016/j.jacc.2023.05.061.
2
A systematic review of cardiac clinical trials.一项关于心脏临床试验的系统评价。
Prog Biomed Eng (Bristol). 2023 Jul 1;5(3):032004. doi: 10.1088/2516-1091/acdc71. Epub 2023 Jun 22.
3
Diagnostic accuracy of heart auscultation for detecting valve disease: a systematic review.心脏听诊诊断瓣膜疾病的准确性：系统评价。
BMJ Open. 2023 Mar 24;13(3):e068121. doi: 10.1136/bmjopen-2022-068121.
4
The Future of AI-Enhanced ECG Interpretation for Valvular Heart Disease Screening.人工智能增强型心电图解读在瓣膜性心脏病筛查中的未来
J Am Coll Cardiol. 2022 Aug 9;80(6):627-630. doi: 10.1016/j.jacc.2022.05.034.
5
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.rECHOmmend：一种基于心电图的机器学习方法，用于识别心电图检查可发现但尚未诊断的结构性心脏病风险增加的患者。
Circulation. 2022 Jul 5;146(1):36-47. doi: 10.1161/CIRCULATIONAHA.121.057869. Epub 2022 May 9.
6
Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes.用于人工智能预测MitraClip介入治疗结果的二尖瓣图谱
Front Cardiovasc Med. 2021 Dec 10;8:759675. doi: 10.3389/fcvm.2021.759675. eCollection 2021.
7
Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review.计算实验方法的可能应用场景：基于共识的综述。
IEEE J Biomed Health Inform. 2021 Oct;25(10):3977-3982. doi: 10.1109/JBHI.2021.3090469. Epub 2021 Oct 5.
8
Medical robotics-Regulatory, ethical, and legal considerations for increasing levels of autonomy.医疗机器人——自主程度不断提高的监管、伦理和法律考虑因素。
Sci Robot. 2017 Mar 15;2(4). doi: 10.1126/scirobotics.aam8638.
9
Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.基于深度学习的心电图主动脉瓣狭窄检测算法。
J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21.
10
Artificial intelligence for detecting mitral regurgitation using electrocardiography.利用心电图检测二尖瓣反流的人工智能技术。
J Electrocardiol. 2020 Mar-Apr;59:151-157. doi: 10.1016/j.jelectrocard.2020.02.008. Epub 2020 Feb 27.

人工智能在心脏瓣膜疾病中的应用：诊断、创新与治疗。一篇最新综述。

Artificial intelligence in heart valve disease: diagnosis, innovation and treatment. A state-of-the-art review.

作者信息

Bamford Paul, Abdelrahman Amr, Malkin Christopher J, Cunnington Michael S, Blackman Daniel J, Ali Noman

机构信息

Interventional Fellow.

Interventional Cardiologist.

出版信息

Br J Cardiol. 2024 Aug 6;31(3):031. doi: 10.5837/bjc.2024.031. eCollection 2024.

DOI:10.5837/bjc.2024.031
PMID:39917554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11795922/
Abstract

In recent years, artificial intelligence (AI) has been used to improve the precision of valvular heart disease diagnosis and treatment. It has the ability to identify and risk stratify patients with valvular heart disease and holds promise in improving the innovation of new treatments through shorter, safer and more effective clinical trials. AI can help to guide the treatment of patients with valvular heart disease, by aiding in optimal device selection for transcatheter valvular interventions and, potentially, predicting the risk of specific complications. This review article explores the various potential applications of AI in the diagnosis and treatment of valvular heart disease in more detail.

摘要

近年来，人工智能（AI）已被用于提高心脏瓣膜病诊断和治疗的精准度。它有能力识别心脏瓣膜病患者并进行风险分层，且有望通过更短、更安全和更有效的临床试验推动新治疗方法的创新。人工智能可以辅助选择经导管瓣膜介入治疗的最佳器械，并有可能预测特定并发症的风险，从而帮助指导心脏瓣膜病患者的治疗。这篇综述文章更详细地探讨了人工智能在心脏瓣膜病诊断和治疗中的各种潜在应用。