用脑区影像数据精准区分阿尔茨海默病早期阶段，提升诊疗效率。
MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework
- 仅聚焦海马体与杏仁核，通过特征选择降低维度噪声。
- 分类准确率达88.46%，有效区分早期与晚期轻度认知障碍。
- 适合临床辅助诊断，尤其关注阿尔茨海默病早期筛查人群。
阿尔茨海默病（AD）发展经历从早中期轻度认知障碍（EMCI）到晚中期轻度认知障碍（LMCI）直至最终确诊的阶段。准确识别这些阶段，尤其是区分EMCI与LMCI，对开发前痴呆治疗方案至关重要，但因影像特征细微且重叠，仍具挑战性。本研究提出一种极简特征机器学习框架，利用结构磁共振成像（MRI）数据，聚焦海马体和杏仁核作为感兴趣区域。该框架通过特征选择缓解维度灾难，采用区域特异性体素信息，并创新数据组织方式以减少噪声。方法结合主成分分析（PCA）与t-SNE等降维技术及先进分类器，实现最高88.46%的分类准确率。结果表明，该框架具备高效、精准划分AD进展阶段的潜力，并为临床应用提供重要参考。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) and eventually to AD. Accurate identification of these stages, especially distinguishing LMCI from EMCI, is crucial for developing pre-dementia treatments but remains challenging due to subtle and overlapping imaging features. This study proposes a minimal-feature machine learning framework that leverages structural MRI data, focusing on the hippocampus and amygdala as regions of interest. The framework addresses the curse of dimensionality through feature selection, utilizes region-specific voxel information, and implements innovative data organization to enhance classification performance by reducing noise. The methodology integrates dimensionality reduction techniques such as PCA and t-SNE with state-of-the-art classifiers, achieving the highest accuracy of 88.46%. This framework demonstrates the potential for efficient and accurate staging of AD progression while providing valuable insights for clinical applications.
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