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在低成本,查CT分检查中,机会主义自动化大动脉结石化评估

Ananya Subramaniam¹, Hao Wu², Sepideh Azarianpour²

¹Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA. axs1189@case.edu.

Scientific reports

|January 9, 2026

概括

我们开发了一种新的人工智能方法,用于在CT分数扫描上检测大动脉结石化 (AVC),从而能够在老年人群中早期识别大动脉狭窄 (AS) 和潜在的心力衰竭风险.

科学领域:

心脏病学心脏病学
放射学放射学是一门学科.
人工智能在医学中的应用

背景情况:

大动脉狭窄 (AS) 是最常见的心脏病,特别影响人口老龄化,并可能导致心力衰竭.
对AS的早期检测和干预至关重要,跨导管大动脉置换 (TAVR) 成为关键治疗方法.

研究的目的:

通过使用非对比CT分 (CTCS) 查检查来确定大动脉结石化 (AVC) 来解决早期AS检测的未满足需求.
开发和验证深度学习模型,用于在CTCS图像中自动检测和量化AVC.

主要方法:

创建了一个多任务深度网络,以识别CTCS图像中感兴趣的大动脉区域 (ROI).
在预测的投资回报率内应用了阿加斯顿标准来量化化,结果与心脏病学家的评估进行了比较.
该模型被追溯应用于2000多名接受CTCS查的患者队列.

主要成果:

深度网络在确定ROI和计算AVC的Agatston分数方面与心脏病学家达成了强烈一致.
对查队列的分析显示,AVC发生在20.6%的男性和22.2%的女性中.
根据指导方针,该队列中3.53%的男性和9.04%的女性将被归类为患有严重的AS.

结论:

开发的AI方法显示出在低成本的CTCS扫描上检测AVC的前景,从而促进AS早期诊断.
这种方法有可能通过为AS患者提供及时的转诊和治疗,对公共卫生产生重大影响.
需要进一步评估,以将这种AI工具整合到临床实践中,以便广泛查AS.

关键词:

大动脉的化化.大动脉门狭窄症在CT测试中,分数得分.参数回归的参数回归方法

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