Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.
Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.
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DOI：
10.3390/medsci11010020
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发表时间：
2023-02-24
期刊：
影响因子：
--
通讯作者：
Dastidar A
中科院分区：
文献类型：
--
作者：
Doolub G;Mamalakis M;Alabed S;Van der Geest RJ;Swift AJ;Rodrigues JCL;Garg P;Joshi NV;Dastidar A
冠状动脉疾病（CAD）仍然是世界范围内死亡和发病的主要原因，并且与相当大的经济负担相关。在老龄化、多病人群中，开发可靠、一致、低风险、非侵入性的CAD诊断方法变得越来越重要。该领域多种心脏模式的发展在很大程度上解决了这一难题，不仅提供了关于解剖疾病的信息，如冠状动脉计算机断层扫描血管造影术（CCTA），而且还提供了关于功能评估的关键细节，例如，使用应力心脏磁共振（S-CMR）。人工智能（AI）领域正在以惊人的速度发展，特别是在医疗保健领域。在医疗保健领域，在各种临床环境中使用人工智能和机器学习（ML）实现了关键的里程碑，从智能手表检测心律失常到视网膜图像分析和皮肤癌预测。最近，我们看到人们对在心血管成像领域开发基于人工智能的技术产生了浓厚的兴趣，因为人们认为，ML方法有可能通过将计算机算法应用于具有多维变量的大型数据库来克服当前风险模型的一些局限性，从而能够包含复杂的关系来预测结果。在本文中，我们回顾了当前关于人工智能在CAD评估中的各种应用的文献，重点是多模态成像，然后讨论了该领域在心脏病学中继续发展时可能遇到的未来前景和关键挑战。
Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.
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