AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database

Brain Disorders

Pindong Chen¹  Chunshui Yu²  Yuying Zhou²  Hongwei Yang²  Nianming Zuo²  Pan Wang²  Zengqiang Zhang³  Xi Zhang³  Yong Liu³  Ying Han³  Bing Liu³  Tianzi Jiang⁴  Dawei Wang⁴  Tong Han⁴  Yida Qu⁴  Jie Lu⁴  Bo Zhou⁵  Xiaopeng Kang⁶  Chengyuan Song⁷  Kai Du⁸  Hongxiang Yao⁹ 

[1] Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;;Brainnetome Center &Branch of Chinese PLA General Hospital, Sanya, China;Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China;Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China;Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China;Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China;

关键词: Alzheimer's disease (AD);    Diffusion tensor imaging (DTI);    Multisite;    Automated fiber quantification (AFQ);    Classification;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: Diffusion tensor imaging (DTI) has been widely used to identify structural integrity and to delineate white matter (WM) degeneration in Alzheimer's disease (AD). However, the validity and replicability of the ability to discriminate AD patients and normal controls (NCs) of WM measures are limited due to the use of small cohorts and diverse image processing methods. As yet, we still do not have a clear idea of whether WM characteristics are biomarkers for AD. Methods: We conducted a competition with diffusion measurements along 18 fiber tracts as features extracted via the automated fiber quantification (AFQ) method based on one of the largest worldwide DTI multisite biobanks (862 individuals, consisting of 279 NCs, 318 ADs, and 265 MCIs). After quality control, 825 subjects (276 NCs, 294 ADs, and 255 MCIs) were divided into a public training set (N=700) and a private testing set (N=125). Forty-eight teams submitted 130 solutions that were estimated on the private testing samples. We reported the final results of the top ten models. Results: The performance of white matter features in AD classification was stable and generalizable, which indicated the potential of WM to be a biomarker for AD. The best model achieved a prediction accuracy of 82.35% (with a sensitivity of 86.36% and a specificity of 78.05%) on the private testing set. The average accuracy of the top ten solutions was over 80%. Conclusions: The results of this competition demonstrated that DTI is a powerful tool to identify AD. A larger dataset and additional independent cohort cross-validation may improve the discriminant performance and generalization power of the classification models, thus revealing more precise disease severity factors associated with AD. For this purpose, we have released this database (https://github.com/YongLiuLab/AI4AD_AFQ) to the community, with the expectation of new solutions for the accurate diagnosis of AD.

【 授权许可】

Unknown