自主监督的基础变压器模型的深度表现学习,使得基于心电图的高效评估能够以有限的标签对心脏和冠状动脉功能进行有效评估
medRxiv : the preprint server for health sciences
|November 14, 2023
概括
在心电图 (ECG) 上使用自主监督学习 (SSL) 的深度学习模型现在可以识别心肌流量储备 (MFR) 和左心室喷射率 (LVEF) 的损伤. 这些来自心电图的信号预测了接受压力测试的患者的死亡风险.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 用于心脏分析的深度学习需要广泛的标记数据,这是罕见疾病的局限性.
- 标准的心血管检测很难识别微血管和血管运动功能障碍.
- 定子发射断层扫描 (PET) 提供了黄金标准数据,但成本昂贵,可用性有限.
研究的目的:
- 开发一种自主监督学习 (SSL) 模型,使用ECG检测微血管和左心室功能障碍.
- 评估开发的AI模型的诊断和预后能力.
主要方法:
- 使用未标记的心电图数据 (N=800,035) 预训练了一种视力变压器基础模型.
- 该模型为心肌流量储备受损 (AI-MFR) 和左心室排气分数受损 (AI-LVEF) 的检测进行了微调.
- 在PET MPI队列 (N=1031) 中测试了诊断准确性,并在PET和SPECT MPI队列 (N=7666) 中进行了预后评估.
主要成果:
- 与 de novo 训练 (AUROC 0.632) 相比,SSL 预训练显著提高了 AI-MFR 诊断准确度 (AUROC 0.758).
- 通过SSL预训练,AI-MFR和AI-LVEF的诊断性能得到了改善.
- 异常的AI-MFR与所有测试队列的死亡风险增加显著相关.
结论:
- 视觉转换器的SSL预训练使ECG能够识别MFR和LV功能受损的信号.
- 这些来自心电图的信号是进行非侵入性压力测试的患者预后的强有力的预测因素.
- 这种方法可能会导致心脏功能障碍的更有效的诊断和管理.
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