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阿尔茨海默病诊断在元宇宙中的疾病
Jalal Safari Bazargani1, Nasir Rahim2, Abolghasem Sadeghi-Niaraki1
1Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Korea.
这项研究引入了一种新的虚拟现实 (VR) 和人工智能 (AI) 系统,用于早期阿尔茨海默氏症 (AD) 诊断. 综合方法在使用认知分数和MRI数据检测AD进展方面取得了高准确性.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人与计算机的交互
背景情况:
- 早期阿尔茨海默病 (AD) 诊断对于管理至关重要,但缺乏自动化,非侵入性方法.
- 现有的虚拟现实 (VR) 研究用于AD诊断往往缺乏全面的认知测试和老年友好的设计,产生不可靠的结果.
研究的目的:
- 设计一个老年人友好的VR环境,整合AD诊断的关键认知评估.
- 开发和训练一个3D卷积神经网络-机器学习 (3DCNN-ML) 模型用于使用认知得分和MRI数据预测AD.
主要方法:
- 在VR环境中实施阿尔茨海默氏病评估尺度-认知 (ADAS-Cog),蒙特利尔认知评估 (MoCA) 和迷你精神状态检查 (MMSE).
- 在阿尔茨海默病神经成像倡议2 (ADNI2) 数据集上训练3DCNN-ML模型,包括认知分数和多模式MRI数据.
- 通过定制问卷评估VR系统的用户体验,界面和舒适性.
主要成果:
- CS-MRI实验表现出高性能,97%的精度,95%的回忆,95%的F1分数,96%的AUC和94%的准确性.
- 虚拟现实系统被评为可以接受的,提供了愉快和沉浸式的用户体验,对界面和机械有积极的反.
- 参与者的反表明了在图形,声音质量和长时间使用耳机的舒适性方面需要改进的领域.
结论:
- 综合系统分析了3D大脑体积和认知分数,显示了早期AD检测和及时干预的前景.
- 这种VR和AI驱动的方法为推进AD诊断技术提供了宝贵的见解.
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