Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis
Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis
复制标题
DOI：
10.1073/pnas.2020620118
复制
发表时间：
2021-06-15
影响因子：
11.1
通讯作者：
Yaniv, Yael
中科院分区：
文献类型：
--
作者：
Elul, Yonatan;Rosenberg, Aviv A.;Yaniv, Yael
尽管人工智能（AI）系统有着巨大的前景，但在日常医学实践中尚未普及，这主要是由于医疗从业者的几个关键需求未得到满足。这些问题包括缺乏有临床意义的解释，处理未知医疗条件的存在，以及系统局限性的透明度，无论是在统计性能方面还是在识别系统预测无关的情况方面。我们将这些未满足的临床需求描述为机器学习（ML）问题，并使用尖端的ML技术系统地解决它们。我们专注于心电图（ECG）分析，作为AI具有巨大潜力的示例领域，并解决两项具有挑战性的任务：从ECG中检测已知和未知心律失常的异质混合，以及从记录在间歇性心律失常患者中的正常窦性心律片段中识别潜在的心脏病理学。我们通过模拟在大规模人群中筛选心律失常来验证我们的方法，同时遵守统计学显著性要求。具体来说，我们的系统1）可视化ECG段的每个部分对于最终模型决策的相对重要性; 2）维持对其样本外性能的指定统计约束，并为其预测提供不确定性估计; 3）处理包含未知节律类型的输入;以及4）处理来自未见过的患者的数据，同时还标记模型的输出对于特定患者不可用的情况。这项工作是克服目前阻碍人工智能融入心脏病学和医学临床实践的局限性的重要一步。
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system's limitations, both in terms of statistical performance as well as recognizing situations for which the system's predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model's outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general.