使用深度学习将心电图与血液动力学上显著的大动脉流的回声心脏学参数联系起来
Yi-Ting Li1, Kuang-Chien Chiang2, Alexander Te-Wei Shieh2
1School of Medicine, College of Medicine, National Cheng Kung University, Tainan.
Acta Cardiologica Sinica
|November 25, 2024
概括
深度学习模型现在可以使用心电图 (ECG) 检测大动脉吐患者的左心室重塑和功能障碍. 这项技术可能有助于早期检测和查,特别是在心声回声学有限的地方.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 大动脉反 (AR) 在心电图 (ECG) 中对深度学习 (DL) 检测提出了挑战.
- 电脑心电图能在多大程度上检测出与AR相关的左心室 (LV) 重塑和功能障碍仍然不清楚.
研究的目的:
- 评估基于DL的心电图模型在预测与血液动力学显著AR相关的LV重塑参数方面的能力.
- 评估DL模型对LV功能障碍和重塑的关键指标的诊断性能.
主要方法:
- 在大动脉手术前,从573名中度至重度AR患者中回顾收集了1457个12ECG.
- 一个基于ResNet的DL模型被开发出来,用于预测来自ECG的LV喷射分数,尺寸,质量和体积指数.
- 模型性能使用五倍交叉验证和持久测试集进行了评估.
主要成果:
- DL模型对各种LV改造参数,包括LVEF (AUROCs 0.77-0.87) 和维度 (AUROCs 0.74-0.85),表现出显著的预测能力.
- 该模型在LVEDD (>65毫米),LVESDi (>30毫米/米2),LVESVi (>45毫米/米2),LVEDVi (>99毫米/米2),LVMi (>158毫米/米2) 和BAV (0.74-0.85) 中取得了显著的AUROC.
- 莎普利的添加式扩展表明该模型主要利用QRS复合体进行预测.
结论:
- DL模型可以在具有显著AR的患者中识别ECG和LV重塑/功能障碍之间的相关性.
- 使用DL的心电图分析可以促进早期检测LV功能障碍,并指导对心声回声图的需求.
- 这种方法在适时的心声回声评估可能具有挑战性的环境中尤为有价值.
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