Table_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.DOCX

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

遗传算法已被证实具备探索广阔解空间的能力，并能够处理大量输入特征。本研究假设将此类算法应用于^{18}F-氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）可能有助于通过筛选最具意义的特征和自动化诊断过程，从而在阿尔茨海默病（AD）和额颞叶痴呆（FTD）的诊断中发挥效用。本研究旨在开发针对诊断过程中三大主要问题的算法：区分AD或FTD患者与健康对照（HC）、区分行为性FTD（bvFTD）与AD、以及区分原发性进行性失语症（PPA）的变体。通过将K-近邻算法和朴素贝叶斯网络作为适应度函数进行定制，开发了遗传算法，并与主成分分析（PCA）进行了比较。在同一样本内进行了K折交叉验证，并使用ADNI-3样本进行了外部验证。对于区分AD与HC的算法进行了外部验证。我们的研究支持了FDG-PET成像技术的应用，该技术对于AD、FTD及其相关疾病的诊断具有极高的准确率。遗传算法通过最小特征集识别了最具意义的特征，这对于脑部FDG-PET图像的自动化评估可能具有重要意义。总体而言，本研究为利用脑部代谢进行神经退行性疾病的自动化和优化诊断做出了贡献。