Slomka Piotr J, Dey Damini, Sitek Arkadiusz, Motwani Manish, Berman Daniel S, Germano Guido
a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
b Biomedical Imaging Research Institute , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
Expert Rev Med Devices. 2017 Mar;14(3):197-212. doi: 10.1080/17434440.2017.1300057.
Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.
非侵入性成像在心血管疾病患者的管理中起着关键作用。尽管主观视觉解读仍是临床的主要手段，但定量分析有助于进行客观的、基于证据的管理，并推动临床研究的进展。这促使了旨在实现全自动图像处理和定量分析的计算及软件工具的发展。与此同时，机器学习技术已被用于快速整合大量临床和定量成像数据，以提供高度个性化的基于个体患者的结论。涵盖领域：本综述总结了心脏病学中自动定量成像的最新进展，并描述了纳入机器学习原理的最新技术。该综述重点关注广泛应用于临床的心脏成像技术。它还讨论了这些工具在主流临床实践中应用的关键问题和障碍。专家评论：心脏成像的全自动处理和高级计算机解读正在成为现实。将机器学习应用于每次扫描产生的大量定量数据，并与临床数据相结合，也有助于转向更针对患者个体的解读。这些发展不太可能取代解读医生，但将为他们提供高度准确的工具来检测疾病、进行风险分层并优化针对患者个体的治疗。然而，随着每一项技术进步，我们越来越远离对人类的依赖，越来越接近全自动机器解读。
