使用可解释的人工智能在痴呆症患者中构建结构性脑异常的个性化表征
Esten H Leonardsen1,2, Karin Persson3,4, Edvard Grødem5,6
1Department of Psychology, University of Oslo, Oslo, Norway. estenhl@uio.no.
NPJ digital medicine
|May 2, 2024
概括
可解释的人工智能 (AI) 使用大脑MRI扫描上的深度学习现在可以区分痴呆症患者和健康个体. 这项技术有助于了解疾病的进展,并为神经系统疾病的精准医学提供了潜力.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 使用MRI预测神经系统疾病的深度学习模型显示出希望,但由于不透明,缺乏临床影响.
- 了解这些AI模型的决策过程对于临床信任和应用至关重要.
研究的目的:
- 开发一种可解释的AI模型,以区分痴呆症患者与健康对照者,使用结构性脑MRI.
- 为了验证模型的发现与已知的痴呆症相关的大脑异常.
- 评估可解释AI在预测轻度认知障碍患者痴呆过渡中的有用性.
主要方法:
- 卷积神经网络 (CNN) 在结构性大脑MRI扫描上进行训练.
- 用层次相关性传播 (LRP) 来生成模型预测的个体级解释.
- 可解释模型应用于轻度认知障碍患者的纵向数据集.
主要成果:
- 人工智能模型成功地将痴呆症患者与健康对照区分开来.
- 模型解释与痴呆症中大脑结构变化的已知知识保持一致.
- 来自该模型的空间丰富的解释有助于预测痴呆症过渡和描述疾病表现.
结论:
- 通过CNN和LRP,可解释AI增强了基于MRI的神经系统疾病深度学习的临床实用性.
- 这种方法提供了对个体患者病情的生物学相关见解.
- 这项研究强调了可解释的AI在推进痴呆症和相关疾病的精准医学方面的潜力.
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