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预测大脑新陈代谢的变化和从轻度认知障碍到痴呆症的进展,使用多任务深度学习模型和可解释的AI

Fernando García-Gutiérrez¹, Laura Hernández-Lorenzo¹, María Nieves Cabrera-Martín²

¹Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain.

|June 28, 2024

概括

这项研究使用人工智能通过分析大脑新陈代谢变化来预测阿尔茨海默病的进展,识别轻度认知障碍的个体,这些个体将以高精度发展为痴呆症.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
人工智能的人工智能

背景情况:

预测阿尔茨海默病 (AD) 的进展对于早期干预至关重要.
目前在AD中的AI方法通常使用横截面数据,限制预测能力.
模拟大脑新陈代谢的动态变化为AD预测提供了一个新的途径.

研究的目的:

研究模拟大脑新陈代谢变异的潜力,以预测AD进展.
识别患有轻度认知障碍 (MCI) 的个人,这些人将进展为痴呆症 (pMCI).
利用深度学习和可解释的人工智能来提高预测准确度.

主要方法:

分析了来自1617名阿尔茨海默氏病神经成像计划 (ADNI) 参与者的[18F]-氧葡萄糖正子发射断层扫描 (FDG-PET) 数据.
深度学习 (DL) 模型的开发,以预测大脑代谢的变化并识别pMCI.
应用多任务学习和可解释AI (XAI) 来改进和解释模型.

主要成果:

确认大脑低代谢,阿尔茨海默病进展和认知衰退之间的强烈关联.
综合代谢数据改善了pMCI检测 (灵敏度为88.4%,特异性为86.9%).
作为关键预测因素,XAI强调了海马体,环状皮质和皮下结构.

结论:

通过结合大脑代谢动态来进行AD预测建模的新方法.
多任务学习框架有效地识别了患AD进展高风险的个体.
XAI为预测AD进展的神经生物学基础提供了洞察力.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的疾病人工智能的人工智能是人工智能.自动模式识别自动化模式识别深度学习是一种深度学习.机器学习是机器学习.神经退行性疾病的神经退行性疾病神经成像是一种神经成像.阳位电子发射断层扫描 (PET).

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