OBJECTIVE Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. METHODS A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. RESULTS The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. CONCLUSION The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. SIGNIFICANCE The developed scheme is expected to advance the clinical research and treatment of MCI patients.

中文翻译：

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基于纵向磁共振成像的字典学习预测轻度认知障碍向阿尔茨海默病的进展

目标 有效预测轻度认知障碍 (MCI) 向阿尔茨海默病 (AD) 的进展对于 AD 的早期干预和管理非常重要。我们研究的目的是开发一种基于纵向结构磁共振成像的 MCI 进展预测系统。方法 从阿尔茨海默病神经影像学倡议 (ADNI) 收集了总共 164 名具有纵向数据的 MCI 患者。预处理后，应用判别字典学习框架来区分 MCI 补丁，避免感兴趣区域的分割。然后，将患者归类为更严重萎缩斑块的比例计算为他或她的特征，以输入到简单的支持向量机中。最后，通过四重交叉验证 (CV) 预测了一个新主题，并确定受试者工作特征曲线（AUC）下的面积。结果四倍 CV 后的平均准确度和 AUC 值分别为 0.973 和 0.984。还研究了来自一两个时间点的数据的影响。结论 所提出的预测系统在预测 MCI 患者的进展方面取得了理想和可靠的性能。此外，使用纵向数据预测 MCI 进展更有效和准确。意义 所开发的方案有望推进 MCI 患者的临床研究和治疗。还研究了来自一两个时间点的数据的影响。结论 所提出的预测系统在预测 MCI 患者的进展方面取得了理想和可靠的性能。此外，使用纵向数据预测 MCI 进展更有效和准确。意义 所开发的方案有望推进 MCI 患者的临床研究和治疗。还研究了来自一两个时间点的数据的影响。结论 所提出的预测系统在预测 MCI 患者的进展方面取得了理想和可靠的性能。此外，使用纵向数据预测 MCI 进展更有效和准确。意义 所开发的方案有望推进 MCI 患者的临床研究和治疗。