Guo Ling, Pressman Gregg S, Kieu Spencer N, Marrus Scott B, Mathew George, Prince John, Lastowski Emileigh, McDonough Rosalie V, Currie Caroline, Tiwari Urvi, Maidens John N, Al-Sudani Hussein, Friend Evan, Padmanabhan Deepak, Kumar Preetham, Kersh Edward, Venkatraman Subramaniam, Qamruddin Salima
Eko Health, Inc, Emeryville, California, USA.
Jefferson Einstein Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
JACC Adv. 2025 Mar;4(3):101619. doi: 10.1016/j.jacadv.2025.101619. Epub 2025 Feb 20.
Asymptomatic left ventricular systolic dysfunction (ALVSD) affects 7 million globally, leading to delayed diagnosis and treatment, high mortality, and substantial downstream health care costs. Current detection methods for ALVSD are inadequate, necessitating the development of improved diagnostic tools. Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD.
The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients.
2,960 adults undergoing echocardiography from 4 U.S. health care networks were enrolled in this multicenter observational study. Patient data were captured using a digital stethoscope, and echocardiograms were performed within 1 week of data collection. The algorithm's performance was compared against echocardiographic EF (EF measurements, categorizing EF as normal and mildly reduced [>40%] or moderate and severely reduced [≤40%]).
The CNN model demonstrated an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 77.5%, specificity of 78.3%, positive predictive value of 20.3%, and negative predictive value of 98.0%. Among those with an abnormal artificial intelligence screen but EF >40% (false positives), 25% had an EF between 41%-49% and 63% had conduction/rhythm abnormalities. Subgroup analyses indicated consistent performance across various demographics and comorbidities.
The CNN model, utilizing a digital stethoscope, offers a noninvasive and scalable method for early detection of individuals with EF ≤40%. This technology has the potential to facilitate early diagnosis and treatment of heart failure, thereby improving patient outcomes.
无症状左心室收缩功能障碍（ALVSD）在全球影响着700万人，导致诊断和治疗延迟、高死亡率以及大量的下游医疗保健成本。目前用于ALVSD的检测方法并不充分，因此需要开发改进的诊断工具。最近，基于心电图的算法在检测ALVSD方面显示出了前景。
作者开发并验证了一种卷积神经网络（CNN）模型，该模型使用单导联心电图和数字听诊器捕获的心音图输入，以评估其在一大群患者中检测射血分数（EF）低至可采取行动水平的个体的效用。
来自美国4个医疗保健网络的2960名接受超声心动图检查的成年人参与了这项多中心观察性研究。使用数字听诊器采集患者数据，并在数据收集后1周内进行超声心动图检查。将该算法的性能与超声心动图EF（EF测量值，将EF分为正常和轻度降低[>40%]或中度和重度降低[≤40%]）进行比较。
CNN模型的受试者操作特征曲线下面积为0.85，灵敏度为77.5%，特异性为78.3%，阳性预测值为20.3%，阴性预测值为98.0%。在人工智能筛查异常但EF>40%（假阳性）的患者中，25%的患者EF在41%-49%之间，63%的患者有传导/节律异常。亚组分析表明，该模型在不同人口统计学和合并症中表现一致。
利用数字听诊器的CNN模型为早期检测EF≤40%的个体提供了一种非侵入性且可扩展的方法。这项技术有可能促进心力衰竭的早期诊断和治疗，从而改善患者预后。
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