通过可解释的深度学习框架改善阿尔茨海默氏症诊断:包括神经精神病症状
Shujuan Liu1, Yuanjie Zheng1, Hongzhuang Li1
1School of Information Science and Engineering, Shandong Normal University, Shandong, China.
Neuroscience
|September 14, 2023
概括
这项研究引入了一种混合深度学习模型,用于使用MRI和临床数据来诊断阿尔茨海默病 (AD). 该模型准确地识别了AD,强调了冷漠作为一个关键指标.
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:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病.
- 神经精神症状 (NPS) 在阿尔茨海默病中很常见,并且与其严重程度有关.
- 目前用于将NPS与AD严重程度联系起来的研究方法缺乏直观的方法.
研究的目的:
- 为准确的阿尔茨海默病诊断开发混合深度学习框架.
- 整合多式联络数据,包括神经成像和临床信息.
- 研究特定神经精神病症状在阿兹海默氏症诊断中的作用.
主要方法:
- 一个混合深度学习框架,结合了3D卷积神经网络 (CNN) 和主要组件分析 (PCA).
- 使用的多式输入:结构性MRI,行为评分,年龄和性别.
- 雇佣了SHapley添加式解释 (SHAP) 来解释模型决策.
主要成果:
- 在将AD与认知正常个体分类时,获得了高精度 (0.91) 和曲线下的面积 (0.97).
- 证明了模型在整合成像和非成像数据方面的有效性.
- 鉴定了冷漠作为一个重要的神经精神症状,有助于AD诊断.
结论:
- 拟议的混合深度学习框架为阿尔茨海默病诊断提供了一个强大的方法.
- 漠不关心成为在AD诊断,临床试验和未来研究中考虑的关键因素.
- 多模式数据集成显著提高了阿尔茨海默病的诊断准确度.
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