探索心血管决策支持的跨胸腔心电图 (TTE) 任务的多式大型语言模型
Jianfu Li1, Yiming Li2, Zenan Sun2
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL 32224, USA.
Journal of biomedical informatics
|October 25, 2025
概括
这项研究对心血管决策支持的大型语言模型进行了基准测试,发现特定领域的模型和精细调整的一般模型在回声心脏图解中表现出色,特别是在射出分数估计方面.
科学领域:
- 人工智能在医学中的应用
- 心血管成像分析心血管成像分析
- 机器学习用于医疗保健
背景情况:
- 多模式大语言模型 (LLM) 为推进心血管决策支持系统提供了新的机会.
- 解释复杂的心声回声数据仍然是AI驱动的临床实践增强的关键领域.
研究的目的:
- 系统地评估和对基于心声谱的任务进行各种基础模型的基准测试.
- 评估LLM在心血管应用中的有效性,局限性和临床潜力.
主要方法:
- 三个心血管成像数据集 (EchoNet-Dynamic,TMED2,TTE) 用于评估喷射率 (EF) 预测,视图分类,大动脉狭窄 (AS) 评估和疾病分类.
- 六个多式LLM,包括心血管特异性,医疗领域和一般领域的模型,使用零射击,少数射击和微调策略进行了测试.
主要成果:
- 像EchoClip这样的特定领域模型显示出强大的零射击EF预测 (MAE 10.34).
- 微调显著改善了一般域模型,将MiniCPM-V 2.6的EF MAE降低到31.93,并将视图分类准确度提高到63.05%.
- 专注于心血管和微调模型的表现优于其他模型,特别是在EF估计方面,尽管性能在任务之间有所不同.
结论:
- 特定领域的预训练和模型适应对于心血管决策支持中的有效人工智能至关重要.
- 专注于心血管和微调的LLM在EF估计等复杂任务中表现出卓越的性能.
- 这些发现为将AI整合到临床心血管医学中提供了必要的见解.
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