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深度学习算法用于通过整合CT图像和临床特征来预测腹腔大动脉动脉瘤的快速进展

Se Jin Oh^1,2, Jae-Ik Shin^3,4, Eun Na Kim⁵

¹Department of Thoracic and Cardiovascular Surgery, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea. wpwnn@snu.ac.kr.

Scientific reports

|November 3, 2025

概括

结合CT图像,临床和几何数据的新型深度学习模型显著改善了腹腔大动脉瘤 (AAA) 进展预测. 这种人工智能方法增强了风险分层,以更好地监测患者和及时干预.

科学领域:

心血管成像和人工智能
医学图像分析医学图像分析
无线电学和深度学习

背景情况:

腹腔大动脉瘤 (AAA) 的进展带来了显著的破裂风险.
使用有限的临床参数的传统预测模型对于复杂的因素相互作用是不够的.
准确的预测模型对于及时的临床干预至关重要.

研究的目的:

开发和验证一个端到端的多模式深度学习 (DL) 模型,以提高AAA进展预测.
整合CT图像特征,放射性几何特征和临床数据,以改善风险分层.
为了比较多模式DL模型与传统机器学习方法的性能.

主要方法:

对来自博拉梅医疗中心和首尔国立大学医院的561名AAA患者进行了回顾性研究.
利用了14252张注释的CT轴图像和详细的临床信息.
开发了一种多模式的DL模型,包含ResNet提取的图像特征,放射性几何特征和临床数据.

主要成果:

多模式DL模型实现了0.807的AUC和0.758的准确性,用于预测快速AAA进展 (≥2.5毫米/年).
这显著优于仅使用临床数据 (AUC:0.716) 或几何特征 (AUC:0.715) 的传统模型.
德朗的测试证实了AUC改善的统计学意义.

结论:

由人工智能驱动的多模式方法显著提高了患者特定的AAA风险分层.
与传统方法相比,开发的DL模型为AAA进展提供了优异的预测性能.
这种方法有可能为AAA患者进行更精确的监测和优化临床干预的时间.

关键词:

腹腔大动脉动脉瘤腹腔大动脉动脉瘤在CT成像中使用CT成像.深度学习是一种深度学习.数字健康数字健康多式联运模式模型

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