使用深度学习和大型语言模型对精神疾病进行双表示结构性MRI分类.
Hidir Selcuk Nogay1, Hojjat Adeli2
1Bursa Uludag University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Bursa, Turkey.
Psychiatry research. Neuroimaging
|January 23, 2026
概括
这项研究引入了一种新的双表示结构性MRI框架,使用深度学习来改进精神障碍的分类,如精神分裂症和双相情感障碍,提高诊断准确度.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 由于重叠的症状和微妙的神经解剖学差异,精确区分精神分裂症和双相情感障碍是具有挑战性的.
- 目前的诊断方法可能是主观的,缺乏客观的生物标志物.
- 结构性MRI提供了客观评估的潜力,但需要先进的分析技术.
研究的目的:
- 开发和评估一个双重代表性的结构性MRI框架,以改善精神疾病的分类.
- 为了比较原始MRI切片与使用深度学习的组织细分图的诊断效用.
- 利用大型语言模型 (LLM) 提高神经成像发现的可解释性.
主要方法:
- 利用双重表示框架分析原始T1加权的MRI切片和彩色编码的组织细分图.
- 采用两个独立训练的ResNet-18卷积神经网络 (CNN) 来进行特征提取.
- 应用转移学习 (TL) 和域适应 (DA) 技术,对103名受试者在四组 (健康控制,精神分裂谱,双极性精神障碍,双极性精神障碍没有精神障碍) 的数据集.
- 集成了一个大型语言模型 (LLM) 用于CNN输出的后期分析和解释.
主要成果:
- 与单一表示方法相比,双重表示方法证明了四向分类性能的提高.
- 系统比较揭示了原始与基于细分的MRI输入对分类准确性的差异性贡献.
- 通过LLM辅助解读,可以了解驱动诊断预测的神经解剖特征.
结论:
- 拟议的双重代表性框架提高了精神疾病诊断分类的准确性.
- 将深度学习与LLM可解释性相结合,为透明和信息化的精神病学神经成像工具提供了一个有前途的途径.
- 这种方法有可能支持更客观,更可靠的精神病诊断.
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