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使用深度学习对动脉硬化性大动脉斑块的自动定量分析

Hye Jin Bang¹, Jae-Hyeong Park², Sun Geu Chae³

¹Department of Reliability Assessment, Korea Institute of Machinery & Materials, Daejeon, Korea.

The Korean journal of internal medicine

|August 27, 2025

概括

一个使用U-net的人工智能模型精确地细分了通过食道心声图像 (TEE) 的大动脉斑块. 虽然该模型有助于识别复杂的斑块,但它并没有改善未确定源 (ESUS) 患者的栓塞性中风的心血管事件预测.

科学领域:

心血管成像
医学中的人工智能
脑卒中研究

背景情况:

对于评估源不明的栓塞性中风 (ESUS) 来说,食管间回声学 (TEE) 是至关重要的.
准确评估大动脉斑块的特征对于预测心血管事件至关重要.

研究的目的:

使用TEE图像开发和验证基于U网的自动板块细分模型.
评估该模型在估计主动脉斑块面积 (APA) 和主动脉斑块比率 (APR) 中的临床实用性.
确定该模型是否可以预测ESUS患者的主要心血管和脑血管不良事件.

主要方法:

在711名患者的TEE大动脉图像上训练U-net模型进行斑块细分.
该模型估计了主动脉斑块面积 (APA) 和主动脉斑块比率 (APR).
该模型的性能使用交叉与结合 (IoU) 进行评估,并在单独的ESUS队列中测试心血管事件的预测.

主要成果:

在开发和临床数据集中,U-net模型实现了高准确度,平均IOU为0.997.
该模型有效地区分了复杂和简单的大动脉斑块 (p < 0.001).
通过U- net模型估计的APA和APR没有为ESUS患者的主要心血管和脑血管不良事件提供额外的预测价值.

结论:

这种U-net模型可以从TEE图像中预测复杂的大动脉斑块.
需要进一步以人工智能驱动的斑块特征分析,如形态和可移动性,以改善ESUS的预后.
目前的自动斑块测量不足以预测ESUS中的心血管结果.

关键词:

大动脉人工智能深度学习栓塞性中风通过食道的心声学

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