AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database | |
Qu, Yida1,2; Wang, Pan3; Liu, Bing1,2,16; Song, Chengyuan4; Wang, Dawei5; Yang, Hongwei6; Zhang, Zengqiang7; Chen, Pindong1,2; Kang, Xiaopeng1,2; Du, Kai1,2; Yao, Hongxiang8; Zhou, Bo9; Han, Tong10; Zuo, Nianming1,2; Han, Ying11,13,14,15; Lu, Jie6; Yu, Chunshui12; Zhang, Xi9; Jiang, Tianzi1,2,16; Zhou, Yuying3; Liu, Yong1,2,16,17 | |
发表期刊 | Brain Disorders |
ISSN | 2666-4593 |
2021-02 | |
卷号 | 1期号:1页码:10005 |
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. |
关键词 | Alzheimer's disease (AD) Diffusion tensor imaging (DTI) Multisite Automated fiber quantification (AFQ) Classification |
学科门类 | 工学::控制科学与工程 |
DOI | 10.1016/j.dscb.2021.100005 |
URL | 查看原文 |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[81471120] ; National Natural Science Foundation of China[61633018] ; National Natural Science Foundation of China[81871438] ; National Natural Science Foundation of China[81571062] ; National Natural Science Foundation of China[81400890] |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48755 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Liu, Yong |
作者单位 | 1.Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China 4.Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China 5.Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China 6.Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China 7.Branch of Chinese PLA General Hospital, Sanya, China 8.Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China 9.Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China 10.Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China 11.Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China 12.Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China 13.Beijing Institute of Geriatrics, Beijing, China 14.National Clinical Research Center for Geriatric Disorders, Beijing, China 15.Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China 16.Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China 17.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qu, Yida,Wang, Pan,Liu, Bing,et al. AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database[J]. Brain Disorders,2021,1(1):10005. |
APA | Qu, Yida.,Wang, Pan.,Liu, Bing.,Song, Chengyuan.,Wang, Dawei.,...&Liu, Yong.(2021).AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database.Brain Disorders,1(1),10005. |
MLA | Qu, Yida,et al."AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database".Brain Disorders 1.1(2021):10005. |
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