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可解释的人工智能用于基于神经成像的痴呆症诊断和预后
Sophie A Martin1,2, An Zhao1, Jiongqi Qu1
1UCL Hawkes Institute, University College London, London, WC1E 6BT, UK.
medRxiv : the preprint server for health sciences
|January 27, 2025
概括
可解释的人工智能 (XAI) 方法有助于通过神经成像对人工智能进行痴呆症预测. XAI强调了相关的大脑区域,用于阿尔茨海默病的诊断和轻度认知障碍的预后.
科学领域:
- 神经成像和人工智能的人工智能
- 机器学习在医学中的应用
背景情况:
- 使用人工智能和神经成像进行准确的痴呆症预测是可能的,但"黑子"模型缺乏信任.
- 可解释的人工智能 (XAI) 提供了对模型行为和特征重要性的见解.
- 视觉转换器 (ViT) 是对卷积神经网络 (CNN) 的一个自我解释的替代方案.
研究的目的:
- 在CNN和ViT架构中比较十种XAI方法的有效性.
- 评估XAI在理解痴呆症预测模型中的实用性.
- 为了评估阿尔茨海默病 (AD) 诊断和轻度认知障碍 (MCI) 到AD转化预后的XAI.
主要方法:
- 用T1加权的MRI数据来训练分类和预后模型.
- 系统评估了十种XAI技术.
- 评估了AD诊断和MCI-AD转换预测的模型性能.
主要成果:
- 模型实现了AD诊断的81%平衡精度和MCI预后的67%.
- XAI的输出成功地确定了对AD至关重要的大脑区域.
- XAI为预测MCI转换为AD提供了有价值的见解.
结论:
- XAI 方法可以验证人工智能模型利用相关的神经成像特征.
- XAI产生了有价值的数据,用于进一步的临床分析和对AI的信任.
- 可解释的人工智能提高了人工智能驱动的痴呆症神经成像分析的可解释性和可靠性.
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