通过使用机器学习方法的结构性和功能性核磁共振 (MRI) 来识别遗忘性轻度认知障碍
Hyunyoung Hwang1, Si Eun Kim1, Ho-Joon Lee2
1Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Clinical neurology and neurosurgery
|February 25, 2024
概括
机器学习使用脑成像准确地分类认知障碍. 结合结构性和功能性MRI数据,可以提高轻度认知障碍 (MCI) 和其亚型的诊断准确度.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 认知神经学 认知神经学
背景情况:
- 轻度认知障碍 (MCI) 的早期诊断对于有效治疗至关重要.
- 在临床环境中,区分MCI与正常认知是具有挑战性的.
- 机器学习为提高诊断准确性提供了潜力.
研究的目的:
- 评估机器学习在分类认知无障碍 (CU) 和遗忘性MCI (aMCI) 组中的可行性.
- 在aMCI患者中区分单个和多个记忆障碍.
- 评估结构和功能神经成像数据的综合实用性.
主要方法:
- 利用了50名aMCI和26名CU患者的结构性和静止状态功能性MRI.
- 使用图形理论提取了结构体积和功能连接组形状.
- 应用支持矢量机 (SVM) 算法,用于分类的k-fold交叉验证.
主要成果:
- 具有结构体积的SVM实现了CU与aMCI分类的88.9%准确性.
- 结合结构和功能数据,CU与aMCI的准确性提高到92.9%.
- 在组合数据时,单个和多个记忆障碍的分类准确性从36.7%增加到53.1%.
结论:
- 机器学习整合结构和功能神经成像有效地分类认知水平.
- 与单独的结构数据相比,组合数据显著提高了分类性能.
- 这种方法有助于区分aMCI和CU,并识别aMCI的子类型.
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