Narrative review of the role of artificial intelligence to improve aortic valve disease management.
Narrative review of the role of artificial intelligence to improve aortic valve disease management.
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DOI：
10.21037/jtd-20-1837
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发表时间：
2021-01
影响因子：
2.5
通讯作者：
Steeds RP
中科院分区：
文献类型：
--
作者：
Thoenes M;Agarwal A;Grundmann D;Ferrero C;McDonald A;Bramlage P;Steeds RP
瓣膜性心脏病（VHD）是一种慢性进行性疾病，由于人口老龄化，在西方世界的患病率不断增加。VHD通常在患者出现症状的晚期诊断，并且由于继发性并发症（包括左心室（LV）功能障碍）的发展，包括瓣膜置换术在内的治疗结局可能不理想。人工智能（AI）的临床应用，包括机器学习（ML），不仅可以支持早期和更及时的诊断，还可以加快患者转诊并确保VHD的最佳治疗。由于医生听诊在诊断严重VHD方面缺乏准确性，因此在市售数字听诊器的帮助下的计算机辅助听诊（CAA）改善了心脏杂音的检测和分类。虽然在目前的临床实践中很少使用，但CAA可以以低成本筛查大量人群，具有高准确性，并便于适当的患者转诊。超声心动图仍然是评估和计划管理的下一步，人工智能正在加速训练，通过模式识别和图像分类提高图像质量，以及自动测量多个变量，从而提高准确性。此外，人工智能有可能通过自动警报红旗发现以及处理结果的决策支持来加速患者处置。在管理方面，支持ML的工具在支持全面疾病监测和个性化治疗决策方面具有巨大潜力。使用来自多个来源的数据，包括用于图像变量的人口统计学和临床风险数据以及来自电子医疗记录的电子报告，可以识别与更大风险相关的特定患者表型，或者对VHD进展的估计轨迹进行建模。最后，人工智能算法在规划介入方面具有经证实的价值，通过自动测量从成像数据导出的解剖尺寸来促进经导管瓣膜置换术，以改善瓣膜选择、瓣膜尺寸和输送方法。
Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery.