Sehly Amro, Jaltotage Biyanka, He Albert, Maiorana Andrew, Ihdayhid Abdul Rahman, Rajwani Adil, Dwivedi Girish
Department of Cardiology, Fiona Stanley Hospital, WA 6150 Murdoch, Australia.
Department of Allied Health, Fiona Stanley Hospital, WA 6150 Murdoch, Australia.
Rev Cardiovasc Med. 2022 Jul 19;23(8):256. doi: 10.31083/j.rcm2308256. eCollection 2022 Aug.
Artificial Intelligence (AI) has impacted every aspect of clinical medicine, and is predicted to revolutionise diagnosis, treatment and patient care. Through novel machine learning (ML) and deep learning (DL) techniques, AI has made significant grounds in cardiology and cardiac investigations, including echocardiography. Echocardiography is a ubiquitous tool that remains first-line for the evaluation of many cardiovascular diseases, with large data sets, objective parameters, widespread availability and an excellent safety profile, it represents the perfect candidate for AI advancement. As such, AI has firmly made its stamp on echocardiography, showing great promise in training, image acquisition, interpretation and analysis, diagnostics, prognostication and phenotype development. However, there remain significant barriers in real-world clinical application and uptake of AI derived algorithms in echocardiography, most importantly being the lack of clinical outcome studies. While AI has been shown to match or even best its human counterparts, an improvement in real world outcomes remains to be established. There are also legal and ethical concerns that hinder its progress. Large outcome focused trials and a collaborative multi-disciplinary effort will be necessary to push AI into the clinical workspace. Despite this, current and emerging trials suggest that these systems will undoubtedly transform echocardiography, improving clinical utility, efficiency and training.
人工智能(AI)已经影响到临床医学的各个方面,并预计将给诊断、治疗和患者护理带来变革。通过新颖的机器学习(ML)和深度学习(DL)技术,AI在心脏病学和心脏检查领域取得了重大进展,包括超声心动图。超声心动图是一种广泛应用的工具,仍然是评估许多心血管疾病的一线手段,它拥有大量数据集、客观参数、广泛的可及性以及出色的安全性,是AI发展的理想选择。因此,AI已在超声心动图领域站稳脚跟,在训练、图像采集、解读与分析、诊断、预后评估和表型发展等方面展现出巨大潜力。然而,在超声心动图中AI衍生算法的实际临床应用和采用仍存在重大障碍,最重要的是缺乏临床结局研究。虽然AI已被证明能够与人类同行相媲美甚至更胜一筹,但在实际临床结局方面的改善仍有待确立。此外,法律和伦理问题也阻碍了其发展。需要开展大规模聚焦结局的试验以及多学科协作努力,才能将AI推向临床应用。尽管如此,当前和正在进行的试验表明,这些系统无疑将改变超声心动图,提高临床效用、效率和培训水平。