基于图像的心电图分析深度学习算法预测生物年龄和死亡风险:种族间验证。

IF 2.9 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Journal of Cardiovascular Medicine Pub Date : 2024-11-01 Epub Date: 2024-09-12 DOI:10.2459/JCM.0000000000001670
Youngjin Cho, Ji Soo Kim, Joonghee Kim, Yeonyee E Yoon, Se Young Jung
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引用次数: 0

摘要

背景:心血管风险评估是医疗保健的重要组成部分,可指导预防和治疗策略。在这项研究中,我们开发并评估了一种基于图像的心电图(ECG)分析人工智能(AI)模型,该模型可估算生物年龄和死亡风险:利用首尔国立大学盆唐医院 250 145 名患者的 978 319 份心电图数据集,我们开发了一个深度学习模型,利用打印的 12 导联心电图图像来估计患者的年龄(ECG-Age)以及 1 年和 5 年的死亡风险。我们利用巴西的 CODE-15% 数据集对该模型进行了外部验证:结果:在内部和外部验证数据集中,心电图年龄与实际年龄具有很高的相关性(Pearson's R = 0.888 和 0.852)。在内部验证中,直接死亡率风险预测模型对 5 年和 1 年全因死亡率的曲线下面积(AUC)分别为 0.843 和 0.867。5 年和 1 年心血管死亡率的曲线下面积分别为 0.920 和 0.916。在 CODE-15% 中,预测 5 年和 1 年全因死亡率的死亡率风险 AUC 分别为 0.818 和 0.836。与中性Delta-Age(心电图年龄-年代年龄)组相比,阳性Delta-Age组(5-10、10-15、15-20、>20)的死亡危险比分别为1.88[95%置信区间(CI):1.14-3.92]、2.12(95% CI:1.15-3.92)、4.46(95% CI:2.22-8.96)和7.68(95% CI:3.32-17.76):基于图像的人工智能心电图模型是估算生物年龄和评估全因和心血管死亡风险的可行工具,为利用标准化心电图图像预测长期健康结果提供了一种实用方法。
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Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.

Background: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI) model that estimates biological age and mortality risk.

Methods: Using a dataset of 978 319 ECGs from 250 145 patients at Seoul National University Bundang Hospital, we developed a deep-learning model utilizing printed 12-lead ECG images to estimate patients' age (ECG-Age) and 1- and 5-year mortality risks. The model was validated externally using the CODE-15% dataset from Brazil.

Results: The ECG-Age showed a high correlation with chronological age in both the internal and external validation datasets (Pearson's R = 0.888 and 0.852, respectively). In the internal validation, the direct mortality risk prediction models showed area under the curves (AUCs) of 0.843 and 0.867 for 5- and 1-year all-cause mortality, respectively. For 5- and 1-year cardiovascular mortality, the AUCs were 0.920 and 0.916, respectively. In the CODE-15%, the mortality risk predictions showed AUCs of 0.818 and 0.836 for the prediction of 5- and 1-year all-cause mortality, respectively. Compared to the neutral Delta-Age (ECG-Age - chronological age) group, hazard ratios for deaths were 1.88 [95% confidence interval (CI): 1.14-3.92], 2.12 (95% CI: 1.15-3.92), 4.46 (95% CI: 2.22-8.96) and 7.68 (95% CI: 3.32-17.76) for positive Delta-Age groups (5-10, 10-15, 15-20, >20), respectively.

Conclusion: An image-based AI-ECG model is a feasible tool for estimating biological age and assessing all-cause and cardiovascular mortality risks, providing a practical approach for utilizing standardized ECG images in predicting long-term health outcomes.

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来源期刊
Journal of Cardiovascular Medicine
Journal of Cardiovascular Medicine 医学-心血管系统
CiteScore
3.90
自引率
26.70%
发文量
189
审稿时长
6-12 weeks
期刊介绍: Journal of Cardiovascular Medicine is a monthly publication of the Italian Federation of Cardiology. It publishes original research articles, epidemiological studies, new methodological clinical approaches, case reports, design and goals of clinical trials, review articles, points of view, editorials and Images in cardiovascular medicine. Submitted articles undergo a preliminary review by the editor. Some articles may be returned to authors without further consideration. Those being considered for publication will undergo further assessment and peer-review by the editors and those invited to do so from a reviewer pool. ​
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