一种深度学习方法,用于使用多中心心脏MRI数据自动评估心肌梗塞后悖论性脉冲
Bing-Hua Chen1, Chong-Wen Wu1, Dong-Aolei An1
1Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No.160 PuJian Road, Shanghai, 200127, China.
European radiology
|June 30, 2023
概括
一个深度卷积神经网络 (DCNN) 模型使用心磁共振 (CMR) 图像准确地识别了左心室 (LV) 的悖论性脉动. 这种人工智能方法超过了人类的性能,在心脏病发作治疗后提供了改进的诊断.
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:
- 左心室 (LV) 悖论性脉是心肌梗塞后心脏功能障碍的关键指标.
- 准确识别LV矛盾脉对于指导治疗和预测结果至关重要.
- 当前的诊断方法在敏感性和客观性方面可能存在局限性.
研究的目的:
- 开发和验证深度卷积神经网络 (DCNN) 模型,用于识别LV悖论脉冲.
- 整合多维心脏磁共振 (CMR) 成像数据,以提高诊断准确度.
- 将DCNN模型的性能与人类专家的解释进行比较.
主要方法:
- 一项前性研究涉及401名参与者 (311名患者,90名对照).
- 使用CMR数据开发2D UNet细分模型和DCNN分类模型.
- 使用 2D 和 3D ResNets 从 2 和 3 室图像中提取特征.
- 使用子得分,ROC曲线和混矩阵评估模型准确性.
主要成果:
- 在DCNN模型下,ROC曲线 (AUC) 的高面积为0.97 (培训),0.91 (内部) 和0.83 (外部测试).
- 一个2.5维的多视图模型整合了2和3室图像,表现出比3D模型更高的效率.
- 在DCNN模型显著优于训练医生在区分LV悖论脉 (p <0.05).
结论:
- 一个2.5D多视图DCNN模型有效地整合了2和3室CMR数据,以获得更高的诊断灵敏度.
- 该DCNN模型提供了准确和客观的LV矛盾脉冲后再输液的识别.
- 这种人工智能驱动的方法具有显著的潜力,可以改善前心肌梗塞后的患者管理.
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