Martin-Isla Carlos, Campello Victor M, Izquierdo Cristian, Raisi-Estabragh Zahra, Baeßler Bettina, Petersen Steffen E, Lekadir Karim
Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain.
Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.
Front Cardiovasc Med. 2020 Jan 24;7:1. doi: 10.3389/fcvm.2020.00001. eCollection 2020.
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
心脏成像在心血管疾病(CVD)的诊断中起着重要作用。到目前为止,其作用仅限于对心脏结构和功能进行视觉和定量评估。然而,随着大数据和机器学习的出现,构建直接辅助临床医生诊断CVD的人工智能工具的新机会正在涌现。本文对该领域的近期研究进行了全面综述,并向读者详细介绍了机器学习方法,这些方法可进一步用于实现对大多数CVD更自动化、精确和早期的诊断。