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从半结构冠状动脉CT血管学报告中提取CAD-RADS 2.0的大型语言模型:多机构研究

Dabin Min^1,2, Kwang Nam Jin^3,4, SangHeum Bang³

¹Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, Republic of Korea.

Korean journal of radiology

|August 28, 2025

概括

大型语言模型 (LLM) 准确地从冠状动脉CT血管学 (CCTA) 报告中提取冠状动脉疾病报告和数据系统 (CAD-RADS) 的组件. 在CAD-RADS 2.0数据提取过程中提升了LLM的准确性.

科学领域:

医疗信息学
放射学中的人工智能
心血管成像

背景情况:

冠状动脉扫描 (CCTA) 报告包含诊断冠状动脉疾病 (CAD) 的关键数据.
冠状动脉疾病报告和数据系统 (CAD-RADS) 标准化了CCTA发现的报告.
自动提取CAD-RADS组件可以简化工作流程并提高数据的一致性.

研究的目的:

评估各种大型语言模型 (LLM) 从CCTA报告中提取CAD-RADS 2.0组件的准确性.
评估各种促使策略,包括思维链 (CoT),对LLM绩效的影响.
在多机构数据集上比较多个LLM的性能.

主要方法:

一个由319个综合性,半结构化的CCTA报告组成的多机构数据集.
对CAD-RADS 2.0组件 (狭窄症严重程度,斑块负担,修饰剂) 的参考标准由经过认证的放射科医生制定.
使用零射击,少数射击和Cot提示策略评估了六种LLM.
使用McNemar的测试进行统计比较.

主要成果:

在提取所有CAD- RADS 2.0组件时,LLM表现出高精度,峰值狭窄度的精度高达0. 980 (内部) 和0. 946 (外部).
斑块负荷提取实现了近乎完美的精度 (外部最高为0. 993), 修饰剂检测率始终高 (≥0. 990).
连锁思维 (CoT) 促使几个模型的精度显著提高,特别是GPT-4,在狭窄严重程度上提高了0.192.

结论:

大型语言模型显示出从CCTA报告中精确自动提取CAD-RADS 2.0组件的巨大潜力.
使用思维链提示是提高LLM在此任务中的关键策略.
这些发现支持将LLM整合到放射学工作流程中,以提高效率和数据标准化.

关键词:

在 CAD-RADS 2.0 中冠状动脉CT血管图获取信息大型语言模型促销策略

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