用临床和MRI数据进行阿尔茨海默病诊断的双模型AI框架
Fatih Ciftci1,2,3, Kadriye Yasemin Usta Ayanoğlu3,4, Sajjad Nematzadeh5
1Faculty of Engineering, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, Türkiye.
Frontiers in medicine
|January 26, 2026
概括
本研究介绍了一种混合人工智能框架,将人工神经网络和卷积神经网络结合起来,用于阿尔茨海默病 (AD) 诊断. 人工智能模型在早期风险预测和疾病分期方面取得了很高的准确性,提高了诊断精度.
科学领域:
- 人工智能在医学中的应用
- 神经成像分析分析 神经成像分析
- 计算神经科学是一种神经科学.
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病.
- 早期和准确的AD诊断对于有效的管理至关重要.
- 传统的诊断方法在灵敏度和可扩展性方面存在局限性.
研究的目的:
- 开发和评估用于阿尔茨海默病的混合AI驱动的诊断框架.
- 将临床数据分析与神经成像进行整合,以进行全面的AD评估.
- 为了提高AD诊断的准确性和可解释性.
主要方法:
- 一个混合的人工智能框架,集成了一个人工神经网络 (ANN) 和一个卷积神经网络 (CNN).
- 在临床数据 (1,200名患者,31个特征) 上接受了ANN的培训,用于早期风险预测.
- 美国有线电视新闻网 (CNN) 在MRI图像 (4,876张扫描) 上进行了培训,以确定AD的阶段.
- 用于模型解释性的Grad-CAM可视化.
主要成果:
- 在早期阿尔茨海默病风险预测中,ANN的准确率达到87.08%.
- 美国有线电视新闻网 (CNN) 在分类阿尔茨海默病的阶段方面表现出97%的准确性.
- 混合方法将结构化临床数据与成像分析相结合,以提高诊断精度.
结论:
- 集成的ANN和CNN框架显著提高了阿尔茨海默病的诊断准确性.
- 这种人工智能辅助的方法支持临床决策.
- 未来的研究将专注于轻量级的CNN和可穿戴技术,以实现更广泛的可访问性和更早的干预.
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