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多模式融合学习长 QT 综合征病原性基因型在种族多样化的人口

Joy Jiang¹, Ha My Thi Vy^2,3, Alexander Charney^2,4

¹The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. joy.jiang@icahn.mssm.edu.

NPJ digital medicine

|August 24, 2024

概括

深度学习模型可以通过分析心电图 (ECG) 和健康记录来识别导致先天性长QT综合征 (LQTS) 的遗传突变. 这种方法有助于优先考虑患者进行进一步的诊断评估.

科学领域:

心脏病学心脏病学
遗传学是一个遗传学.
人工智能在医学中的应用

背景情况:

诊断先天性长QT综合征 (LQTS) 是具有挑战性的,因为有限的遗传测试,低患病率,和正常的心电图发现在一些高风险的个体.
准确识别致病变体对于及时干预和管理LQTS至关重要.

研究的目的:

开发和验证一种深度学习模型,用于识别导致LQTS的致病变体的患者.
利用多式联络数据,包括心电图波形和电子健康记录,提高诊断准确度.

主要方法:

开发了一种深度学习方法,整合了心电图 (ECG) 波形和电子健康记录 (EHR) 数据.
该模型最初是使用来自英国生物库 (UKBB) 的数据进行训练的.
随后,对来自西奈山生物银行 (Mount Sinai BioMe Biobank) 的多样性队列进行了微调,采用了分组分层的5倍交叉验证.

主要成果:

微调模型在来自BioMe队列的独立测试数据上表现强.
在接收器操作曲线下的面积 (AUC-ROC) 达到0.83 (95% CI 0.82-0.83).
在精度回忆曲线 (AUC-PRC) 下的面积达到0.29 (95% CI 0.28-0.29).

结论:

多模式融合学习在识别与LQTS相关的致病性遗传突变的个体方面显示出显著的希望.
这种方法可以有效地优先考虑患者进行进一步的临床检查和遗传测试,从而有可能提高诊断产量.
开发的模型提供了一种新的策略,以提高先天性长QT综合征的诊断.

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