用MRI和临床数据来诊断阿尔茨海默病的视觉语言基础模型
Ping-Ju Lin1, Zhaowei Jiang2, Yingxu Liu1
1Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 29, 2025
概括
一个新的AI模型,阿尔茨海默病语言和图像预训练 (ADLIP),整合了脑部扫描和临床数据,以准确诊断阿尔茨海默病. 这种方法对不同患者群体的早期检测和监测具有前途.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 自然语言处理自然语言处理.
背景情况:
- 早期发现阿尔茨海默氏病 (AD) 是具有挑战性的,因为疾病的进展和呈现方式各不相同.
- 使用神经成像和临床数据的多模式方法显示出潜力,但面临整合和概括问题.
研究的目的:
- 开发一种新的视觉语言框架,用于整合3DMRI和AD诊断的临床记录.
- 提高AD诊断模型在不同数据模式中的可通用性和稳定性.
主要方法:
- 开发了阿尔茨海默病语言和图像预训练 (ADLIP),一种视觉语言框架.
- 综合3D T1加权MRI与结构化临床记录,使用多教师培训策略.
- 采用对比的多式模式表示学习来诊断AD.
主要成果:
- 在三类分类和零射击诊断方面,ADLIP在准确性,F1得分和宏观AUC方面超过了基线模型.
- 纵向评估证实了ADLIP与临床疾病轨迹的一致性.
- 该模型在没有再培训的情况下在种族子组中保持了表现,显示出强大的通用性.
结论:
- 对比的多模式表示学习促进了临床上有意义的,可概括的和暂时稳定的AD诊断.
- ADLIP提供零射击预测能力,并与监测应用程序的疾病进展保持一致.
- 该框架通过在不同人群中表现良好来支持公平的临床使用.
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