【摘要】 目的通过海马的MRI影像学分析,研究阿尔茨海默病(AD)患者海马形状的局部异常模式,并构建最优的分类器函数辅助诊断AD。方法对19例AD患者和20名正常老年对照者行MRI扫描,建立海马表面模型,测量海马表面的局部萎缩,构建分类器函数自动判别AD病。结果自动判别的正确率,用留一法交叉验证实验的平均正确率分别为右海马82.1%,左海马92.3%;100次3重交叉验证实验的平均正确率为右海马82.5%,左海马87.2%。结论利用MRI海马的形状特征自动判别AD是可行的。更多还原

【Abstract】 Objective Based on the MRI hippocampal shape analysis, to study the regional pattern differences between Alzheimer’s disease (AD) and normal aging, and build effective classifiers to assist the diagnosis of AD. Methods Conventional MRI were performed in 19 AD patients and 20 age- and gender-matched healthy controls. Then hippocampal surface models were constructed and regional surface deformations were characterized by surface-based measures. Finally, effective classifiers were built to discriminate AD from normal aging. Results The accuracy of automatic recognition were 82.1% and 92.3% by using leave-one-out cross-validation, and similarly the average accuracy of randomized 3-fold cross-validation by 100 times were 82.5% and 87.2% resulted by right and left hippocampus respectively. Conclusion Hippocampal shape analysis is effective for the automatic recognition of AD.更多还原