Yang Hongqin, Mao Jiangbing, Ye Qinyong, Bucholc Magda, Liu Shuo, Gao Wenzhao, Pan Jie, Xin Jiawei, Ding Xuemei
Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China.
Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China.
Front Aging Neurosci. 2024 Apr 15;16:1285905. doi: 10.3389/fnagi.2024.1285905. eCollection 2024.
Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC).
In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders.
Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively.
The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.
新颖性检测（ND，也称为单类分类）是一种机器学习技术，用于识别多数类典型的模式，并将偏差识别为新颖性。在阿尔茨海默病（AD）的背景下，ND可用于检测可能表明认知衰退早期迹象或疾病存在的异常或非典型行为。迄今为止，很少有研究使用ND来区分从健康对照（HC）发展为AD和轻度认知障碍（MCI）的风险。
在这项工作中，使用了来自澳大利亚影像生物标志物与生活方式（AIBL）衰老旗舰研究项目和中国福建医科大学附属协和医院（FMUUH）的两个具有高度异质性数据的不同队列。引入了一个创新框架，该框架具有仅基于HC数据构建的易于解释的ND模型，并提出了一种边界距离（DtB）策略来检测MCI和AD。随后，为非技术利益相关者开发了一个包含所提出框架的基于网络的图形用户界面（GUI）。
我们的实验结果表明，在AIBL和FMUUH数据集中检测AD个体的最佳总体性能是通过将基于高斯混合的ND算法应用于单模态获得的，AUC分别为0.8757和0.9443，灵敏度分别为96.79%和89.09%，特异性分别为89.63%和90.92%。
GUI提供了一个交互式平台，以帮助利益相关者诊断MCI和AD，实现简化的决策过程。更重要的是，所提出的DtB策略可以直观且定量地识别有发展为AD风险的个体。
