Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method of sMCIs and pMCIs based on multi-modality data fusion of single-nucleotide polymorphisms (SNP), ratio of gray matter volume (RGV) obtained by morphometric measures, and sMRI images to predict the progression of AD. We validated the effectiveness of the proposed method by applying it to the task of identifying the disease status on the Alzheimer’s Disease Neuroimaging Initiative dataset. The results showed that the classification performances of our method was better than other state-of-the-art methods, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. The accuracy of our method was better than that of existing classification methods based on multi-modality images, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. Our study demonstrated that compared with unimodal and bimodal data, the method based on trimodal data fusion can better distinguish sMCI and pMCI, obtaining higher prediction accuracy for AD conversion. In addition, as a morphological feature, ratio of gray matter volume played a key role in distinguish of sMCI and pMCI, which can be used for the early diagnosis of AD.

识别进行性轻度认知障碍 （pMCI） 和稳定的轻度认知障碍 （sMCI） 在阿尔茨海默病 （AD） 的早期诊断中起着重要作用，有助于早期治疗以降低转化为 AD 的风险。我们提出了一种基于单核苷酸多态性 （SNP） 多模态数据融合、形态测量获得的灰质体积比 （RGV） 和 sMRI 图像的 sMCIs 和 pMCIs 分类方法，以预测 AD 的进展。我们通过将所提出的方法应用于识别阿尔茨海默病神经影像计划数据集上的疾病状态的任务来验证该方法的有效性。结果表明，该方法的分类性能优于其他最先进的方法，对 pMCI 和 sMCI 的分类准确率达到 94.37%。该方法的准确率优于现有的基于多模态图像的分类方法，对 pMCI 和 sMCI 的分类准确率达到 94.37%。我们的研究表明，与单峰和双峰数据相比，基于三峰数据融合的方法能够更好地区分 sMCI 和 pMCI，获得更高的 AD 转换预测精度。此外，灰质体积比值作为一种形态学特征，在区分 sMCI 和 pMCI 中起关键作用，可用于 AD 的早期诊断。