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一个完全自动化的可解释的预测模型,用于诊断常规无增强CT的毛囊前和毛囊后肺高血压:来自ASPIRE注册表的结果

Turki Nasser Alnasser^1,2,3, Alireza Hokmabadi^1,4, Elliot W Checkley⁵

¹School of Medicine and Population Health, The University of Sheffield, Sheffield S10 2TN, United Kingdom.

European heart journal. Digital health

|January 23, 2026

概括

一种深度学习模型可以自动从CT扫描中对心脏和肺部结构进行细分,以诊断肺高血压 (PH). 这种人工智能工具在识别PH,毛细血管前PH和患有左心病 (LHD) 的PH方面表现出很高的准确性.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
心血管和肺部疾病.

背景情况:

没有增强的胸部CT是肺部疾病评估的标准.
从CT中量化心脏和血管结构可以帮助诊断心力衰竭和肺高血压 (PH).
对心胸结构的准确细分对于定量分析至关重要.

研究的目的:

开发一种深度学习模型,从未增强的CT图像中对心胸结构进行细分和分析.
利用该模型诊断PH,毛细管前PH和与左心病 (LHD) 相关的PH.
评估模型在细分精度和诊断能力方面的表现.

主要方法:

在一个机构队列上使用深度学习开发了一个十二结构心胸细分模型.
使用子相似系数 (DSC) 和类内相关性 (ICC) 评估细分性能.
在外部队列上使用回归分析和ROC曲线评估PH亚型的体积测量和诊断准确性.

主要成果:

该模型实现了9/12结构的DSC≥0.87和10/12结构的ICC>0.95.
外部队列视觉评估将大多数细分结构评为优秀.
对于PH (AUC=0.88),毛囊前PH (AUC=0.84),PH-LHD (AUC=0.86) 的高诊断准确度得到了实现.

结论:

完全自动化的深度学习模型可用于多结构心胸细分在未增强的CT上是可行的.
该模型在预测PH和区分其亚型方面显示出有希望的性能.
这种人工智能驱动的方法有可能改善PH和相关疾病的诊断.

关键词:

一个心脏病患者的心脏病.深度学习是一种深度学习.左心脏病是左心脏病的一种疾病.肺高血压是一种肺高血压.分段化分段化分段化分段化没有增强的CTCT.

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