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在非糖尿病人群中使用心电图进行基于深度学习的2型糖尿病发病的长期风险评估:一项追溯的多中心研究

Junmo Kim¹, Hyun-Lim Yang^2,3, Su Hwan Kim^2,4

¹Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.

EClinicalMedicine

|February 9, 2024

概括

使用心电图 (ECG) 的深度学习模型 (DLM) 确定了患2型糖尿病风险较高的个体. 这种预测工具可以帮助识别风险人群,以便进行早期干预.

科学领域:

心脏病学心脏病学
内分泌学在内分泌学.
人工智能在医学中的应用

背景情况:

糖尿病是一种重大的全球健康挑战.
早期发现2型糖尿病风险的个体对于预防策略至关重要.

研究的目的:

在非糖尿病人群中评估长期患2型糖尿病的风险.
评估深度学习模型 (DLM) 用于使用心电图 (ECG) 数据检测流行的2型糖尿病.

主要方法:

一项回顾性研究包括来自韩国的190,581名参与者 (2001-2022年).
基于ResNet架构的DLM受过训练,可以从ECG中检测2型糖尿病.
使用考克斯比例危险模型进行了对非糖尿病个体的生存分析,根据DLM的预测进行了分类 (糖尿病ECG与非糖尿病ECG).

主要成果:

在检测常见的2型糖尿病 (AUC 0.816内部,0.762外部验证) 中,DLM取得了良好的表现.
被DLM列为"糖尿病心电图"的非糖尿病人显示,发生2型糖尿病的风险显著增加 (HR 2.15内部,1.92外部).
该模型的预测主要受到QRS持续时间和心电图上的P或T波的影响.

结论:

通过DLM识别的心电图模式可以预测没有糖尿病的个体未来患2型糖尿病的风险.
研究结果表明,通过DLM进行的心电图分析是糖尿病风险分层的有希望的非侵入性工具.
建议进行进一步的研究,以探索与糖尿病风险相关的ECG表型,并通过各种数据集验证发现.

关键词:

深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.风险评估风险评估风险评估 2 型糖尿病 2 型糖尿病

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