人工智能增强型心电图作为心脏和非心脏疾病统一筛查工具的前景:一项急诊护理探索性研究。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2024-05-12 eCollection Date: 2024-07-01 DOI:10.1093/ehjdh/ztae039
Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp
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引用次数: 0

摘要

目的:目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,但通常只专注于单一的诊断条件。这项探索性研究旨在调查单一深度学习模型的能力,以根据急诊科收集的单一心电图预测各种心脏和非心脏出院诊断:在本研究中,我们评估了一个经过训练的模型的性能,该模型可预测各种诊断。我们发现,该模型可以可靠地预测 253 个 ICD 代码(81 个心脏疾病和 172 个非心脏疾病),其 AUROC 分数超过 0.8,具有显著的统计学意义:结论:该模型能熟练处理各种心脏和非心脏疾病诊断情况,表明它有潜力成为适用于各种医疗情况的综合筛查工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care.

Aims: 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.

Methods and results: 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.

Conclusion: 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.

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