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使用基于常规T1加权MRI的人工智能进行轻度认知障碍的2型糖尿病的自动诊断

Chang Li¹, Jun Zhang², Bo Xue¹

¹Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.

Frontiers in neurology

|October 27, 2025

概括

机器学习使用脑成像和临床数据准确诊断2型糖尿病 (T2DM) 患者的轻度认知障碍 (MCI). 这种方法有助于早期检测和区分健康对照和没有认知衰退的T2DM患者.

科学领域:

神经成像是一种神经成像.
机器学习机器学习
糖尿病学糖尿病学

背景情况:

2型糖尿病 (T2DM) 增加了痴呆症的风险,特别是在轻度认知障碍 (MCI) 患者中.
与T2DM相关的认知衰退的根本原因尚未完全理解,需要客观的诊断工具.
在T2DM患者中,早期和准确的MCI诊断对于及时干预至关重要.

研究的目的:

开发和验证用于T2DM患者MCI自动诊断的机器学习模型.
为了区分患有MCI的T2DM患者与没有MCI的T2DM患者和健康对照 (HCs).
确定与T2DM认知障碍相关的关键神经影像和临床生物标志物.

主要方法:

利用30名患有MCI的T2DM患者,30名没有MCI的T2DM患者和30名HC的高分辨率T1加权MRI扫描.
使用FreeSurfer提取了全面的全脑结构特征,并应用了MRMR和LASSO来选择特征.
训练了一种随机森林 (RF) 分类器,对所选特征进行分类,并使用5倍交叉验证评估性能.

主要成果:

一个RF模型确定了8个最佳特征 (5个结构性,1个神经心理,1个实验室测试,2个实验室测试) 来诊断T2DM与MCI.
该模型实现了高性能:平均AUC为0.959 (训练) 和0.887 (测试).
关键生物标志物包括脑干体体积,海马体积,糖化血红蛋白和数字跨度测试倒数得分.

结论:

开发的射频模型使用自动化MRI分析准确诊断T2DM与MCI.
该模型为早期检测提供了精确和客观的工具,提高了临床诊断效率.
已识别的生物标志物为T2DM认知障碍的病理生理学提供了见解.

关键词:

这就是为什么MRI是MRI.人工智能 - - 人工智能轻度认知障碍 - - 轻度认知障碍 (MCI)随机森林模型 (RF) 是一种随机森林模型.2型糖尿病是什么?

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