一个无监督的XAI框架用于痴呆症检测与上下文丰富.
medRxiv : the preprint server for health sciences
|June 12, 2025
概括
可解释的人工智能 (XAI) 方法通过将大脑成像功能与人工智能预测集成来改善痴呆症诊断. 这项研究证实了XAI在神经学研究中增强临床决策支持系统的潜力.
科学领域:
- 神经成像和人工智能的人工智能
- 临床决策支持系统 临床决策支持系统
- 可解释的人工智能 (XAI)
背景情况:
- 可解释的人工智能 (XAI) 在临床决策支持系统中提高了AI预测的透明度和可信度,特别是用于脑成像分析.
- 对XAI解释质量的有限验证阻碍了其临床采用,需要强大的评估框架.
- 卷积神经网络 (CNN) 是分析大脑MRI扫描的强大工具,但需要可解释的输出来获得临床信任.
研究的目的:
- 通过将神经解剖学特征与CNN相关性地图相结合,引入和评估评估痴呆症研究中XAI方法的框架.
- 改进XAI解释空间并探索不同的方法来为AI驱动的诊断工具生成临床相关的解释.
- 确定验证的XAI方法在提高基于AI的痴呆症决策支持系统的诊断效率方面的潜力.
主要方法:
- 一个CNN在6个群体 (ADNI,AIBL,DELCODE,DESCRIBE,EDSD,NIFD) 的3253名参与者的脑部MRI扫描上接受了培训.
- 聚类分析使用形态特征作为代理基准真理来基准解释空间配置.
- 实施了三个后期的XAI方法:模型简化,逐例解释和文本解释,随后进行了定性临床评估.
主要成果:
- 以形态丰富的解释空间显示了更好的集群性能,提高了均性和完整性.
- 模型简化解释有效地区分了将转化为痴呆症的参与者和保持稳定的参与者.
- 每个例子解释可视化了潜在的认知轨迹,而文本解释则提供了基于规则的病理发现总结.
结论:
- 该研究成功地完善了XAI解释空间,并证明了各种解释生成方法的实用性.
- 评估的XAI方法显示,在基于人工智能的痴呆症研究决策支持系统中提高诊断效率是有前途的.
- 临床评估证实了这些XAI技术的潜力,强调了未来应用的挑战和机会.
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