The Role of AI in Characterizing the DCM Phenotype.
The Role of AI in Characterizing the DCM Phenotype.
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DOI：
10.3389/fcvm.2021.787614
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发表时间：
2021
影响因子：
3.6
通讯作者：
Carr-White G
中科院分区：
文献类型：
--
作者：
Asher C;Puyol-Antón E;Rizvi M;Ruijsink B;Chiribiri A;Razavi R;Carr-White G
扩张型心肌病通常定义为无冠状动脉疾病的左心室扩张和功能障碍。新出现的证据表明，尽管现代循证心力衰竭治疗取得了明显的治疗成功，但许多患者仍然容易出现主要不良结局。在这个个性化医疗护理的时代，左心室射血分数的常规评估福尔斯不能完全预测这种异质性心肌疾病组的结局的演变和风险，因此，对这种疾病进行表型分型的更精细方法显得至关重要。心脏MRI（CMR）在这方面处于有利地位，不仅因为其诊断实用性，而且因为在全球和区域功能评估中捕获的丰富信息，以及不同疾病状态和患者队列中的独特组织表征。需要先进的工具来利用这些敏感的指标，并与临床，遗传和生化信息整合，以个性化，更临床有用的扩张型心肌病表型的表征。人工智能的最新进展提供了独特的机会，通过增强的精确图像分析任务，多源提取相关特征和无缝集成来影响临床决策，以增强理解，改善诊断和随后的临床结果。特别关注深度学习，人工智能的一个子领域，在成像界引起了极大的兴趣，本文回顾了可以在扩张型心肌病表型中提供更强大的疾病表征和风险分层的主要发展。鉴于其在心脏疾病的非侵入性评估中的前景，我们首先强调了CMR中的关键应用，这些应用旨在实现超出护理评估标准的功能的全面定量测量。同时，我们重新审视了组织表征技术对风险分层的附加价值，展示了克服当前临床工作流程局限性的深度学习平台，并讨论了如何利用它们更好地区分这种表型的风险亚组。本文的最后一部分致力于与成像相关的临床应用，这些应用结合了人工智能，并利用了来自遗传学和相关临床变量的全面丰富的数据，以促进更好的分类，并增强对相关结果的风险预测。
Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.