Asher Clint, Puyol-Antón Esther, Rizvi Maleeha, Ruijsink Bram, Chiribiri Amedeo, Razavi Reza, Carr-White Gerry
Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom.
Front Cardiovasc Med. 2021 Dec 21;8:787614. doi: 10.3389/fcvm.2021.787614. eCollection 2021.
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.
扩张型心肌病传统上定义为在无冠状动脉疾病的情况下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确的治疗成功，但许多患者仍易发生重大不良后果。在这个个性化医疗的时代，传统的左心室射血分数评估在全面预测这一异质性心肌病群体的病情演变和预后风险方面存在不足，因此，对这种疾病进行更精细的表型分析方法显得至关重要。心脏磁共振成像（CMR）在这方面具有优势，不仅因其诊断效用，还因其在整体和区域功能评估中获取的丰富信息，以及在不同疾病状态和患者队列中独特的组织特征描述。需要先进的工具来利用这些敏感指标，并将其与临床、遗传和生化信息整合，以实现对扩张型心肌病表型的个性化且更具临床实用性的特征描述。人工智能的最新进展提供了独特的机会，可通过增强精确的图像分析任务、多源提取相关特征以及无缝整合来影响临床决策，从而增进理解、改善诊断并进而改善临床结局。本文特别关注人工智能的一个子领域——深度学习，它在成像领域引起了广泛关注，回顾了在扩张型心肌病表型中能够提供更可靠的疾病特征描述和风险分层的主要进展。鉴于其在心脏病无创评估中的潜在效用，我们首先强调其在CMR中的关键应用，这些应用能够实现超越常规护理评估标准的全面功能定量测量。同时，我们重新审视组织特征描述技术在风险分层方面的附加价值，展示克服当前临床工作流程局限性的深度学习平台，并讨论如何利用它们更好地区分该表型的高危亚组。本文的最后一部分致力于成像的相关临床应用，这些应用整合了人工智能，并利用了来自遗传学和相关临床变量的全面丰富数据，以促进更好的分类并实现对相关结局的增强风险预测。
