Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.
Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.
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DOI：
10.1161/jaha.123.030377
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发表时间：
2023-10-17
影响因子：
5.4
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中科院分区：
文献类型：
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心脏听诊的成功在医疗专业人员中差异很大，这可能导致错过结构性心脏病的治疗。将机器学习应用于心脏听诊可以解决这个问题，但尽管最近有兴趣，但很少有算法被应用于临床实践。我们评估了一套新的美国食品和药物管理局批准的算法，这些算法通过深度学习在超过15000个心音记录上进行训练。我们验证了来自615个独特的主题的2375个录音的数据集的算法。该数据集是在真实的临床环境中使用市售数字听诊器收集的，由委员会认证的心脏病专家注释，并与超声心动图配对作为金标准。为了在临床实践中对算法进行建模，我们在验证数据库的一个子集上将其性能与10名临床医生进行了比较。我们的算法可靠地检测到结构性杂音，灵敏度为85.6%，特异性为84.4%。当将分析限制在成人的清晰可闻杂音时，性能提高到97.9%的灵敏度和90.6%的特异性。该算法还报告了心动周期内的时间，区分了收缩期和舒张期杂音。尽管为临床医生优化了声学，但该算法的性能仍大大优于临床医生（平均临床医生准确率为77.9%;算法准确率为84.7%）。该算法准确地识别了与结构性心脏病相关的杂音。我们的研究结果说明了算法的一致性和临床医生的观察者间的差异性之间的显着对比。我们的研究结果表明，将机器学习算法应用于临床实践可以改善结构性心脏病的检测，以促进患者护理。
The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration‐cleared algorithms trained via deep learning on >15 000 heart sound recordings. We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board‐certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.