Strodthoff Nils, Lopez Alcaraz Juan Miguel, Haverkamp Wilhelm
Carl von Ossietzky Universität Oldenburg, School VI Medicine and Health Services, Department of Health Services Research, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany.
Charité Universitätsmedizin Berlin, Department of Cardiology and Metabolism, Clinic for Cardiology, Angiology, and Intensive Care Medicine, Berlin, Germany.
Eur Heart J Digit Health. 2024 May 12;5(4):454-460. doi: 10.1093/ehjdh/ztae039. eCollection 2024 Jul.
Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department.
In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner.
The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.
当前用于自动心电图分析的深度学习算法已显示出显著的准确性，但通常狭隘地专注于单一诊断情况。这项探索性研究旨在调查一个单一深度学习模型基于急诊科收集的一份心电图预测多种心脏和非心脏出院诊断的能力。
在本研究中，我们评估了一个经过训练以预测广泛诊断范围的模型的性能。我们发现，该模型能够以统计学上显著的方式在超过0.8的曲线下面积（AUROC）得分的意义上可靠地预测253个国际疾病分类代码（81个心脏相关和172个非心脏相关）。
该模型在处理广泛的心脏和非心脏诊断场景方面表现出熟练程度，表明其作为针对各种医疗情况的综合筛查工具的潜力。
