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从结构化冠状动脉CTA报告中获取增强生成增强的大型语言模型,用于全面的CAD-RADS 2.0分类.

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获取增强生成 (RAG) 增强的大型语言模型 (LLM) 显著提高了从冠状动脉计算机断层扫描血管学 (CCTA) 报告中提取冠状动脉疾病报告和数据系统 (CAD-RADS) 组件的准确性. 这些基于RAG的LLM显示了在临床放射学中自动化和标准化的CCTA报告的潜力.

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科学领域:

  • 医疗成像中的人工智能
  • 放射学 信息学 信息学
  • 在医疗保健中的自然语言处理.

背景情况:

  • 在冠状动脉计算机断层扫描血管学 (CCTA) 中,结构化报告对于准确的诊断和管理至关重要.
  • 大型语言模型 (LLM) 提供了从临床报告中自动提取数据的潜力.
  • 评估LLM在从CCTA报告中提取特定组件和建议方面的表现是必要的.

研究的目的:

  • 评估基于标准和检索增强生成 (RAG) 的LLM在从CCTA报告中提取组件和管理建议方面的性能.
  • 为了比较不同LLM的准确性,包括ChatGPT-5,NotebookLM和RAG适应的ChatGPT-5,与专家放射科医生的评估.
  • 评估LLM在遵守冠状动脉疾病报告和数据系统 (CAD-RADS 2.0) 准则方面的实用性.

主要方法:

  • 分析了320个结构化的CCTA报告,使用三个LLM:标准ChatGPT-5,笔记本LM (基于RAG) 和ChatGPT-5-RAG.
  • 提取CAD-RADS类别,斑块负担,高风险斑块 (HRP),修饰剂,完整得分和管理建议.
  • 与两个专家心血管放射科医生建立的参考标准对比LLM输出.

主要成果:

  • 聊天GPT-5-RAG在所有评估的CAD-RADS 2.0组件中显示出卓越的准确性,包括分类,斑块负担,HRP检测和修饰器.
  • 标准的ChatGPT-5在测试模型中表现最差.
  • 虽然对管理建议的共识很低,但ChatGPT-5-RAG和NotebookLM获得了近乎完美的质量评级.

结论:

  • 用RAG增强的LLM显著提高了提取CAD-RADS 2.0组件的准确性和可靠性,并产生了管理建议.
  • 基于RAG的LLM代表了在临床放射学工作流程中自动化和标准化CCTA报告的有希望的工具.
  • 基于RAG的LLM提供的可解释性和创新性可以增强心血管成像中的临床决策.