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A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples

文献类型：期刊论文

作者Lu, Bin12,13; Li, Hui-Xian12,13; Chang, Zhi-Kai12,13; Li, Le2; Chen, Ning-Xuan12,13; Zhu, Zhi-Chen12,13; Zhou, Hui-Xia11,12,13; Li, Xue-Ying1,10,13; Wang, Yu-Wei12,13; Cui, Shi-Xian1,10,13
刊名JOURNAL OF BIG DATA
出版日期2022-10-13
卷号9期号:1页码:22
关键词Alzheimer's disease Convolutional neural network Magnetic resonance brain imaging Sex differences Transfer learning
DOI10.1186/s40537-022-00650-y
通讯作者Yan, Chao-Gan(yancg@psych.ac.cn)
英文摘要Beyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer's disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen scanners/populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on a dataset of unprecedented size and diversity. A retrospective MRI dataset pooled from more than 217 sites/scanners constituted one of the largest brain MRI samples to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. After transfer learning, the model fine-tuned for AD classification achieved 90.9% accuracy in leave-sites-out cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.5%/93.6%/91.1% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs. 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. In sum, the proposed AD classifier offers a medical-grade marker that has potential to be integrated into AD diagnostic practice.
收录类别SCI
WOS关键词MRI ; SEX ; AGE ; REVEALS ; NETWORK ; CANCER
资助项目Sci-Tech Innovation 2030 -Major Project of Brain Science and Brain-inspired Intelligence Technology[2021ZD0200600] ; National Key R&D Program of China[2017YFC1309902] ; National Natural Science Foundation of China[82122035] ; National Natural Science Foundation of China[81671774] ; National Natural Science Foundation of China[81630031] ; 13th Five-year Informatization Plan of Chinese Academy of Sciences[XXH13505] ; Key Research Program of the Chinese Academy of Sciences[ZDBS-SSW-JSC006] ; Beijing Nova Program of Science and Technology[Z191100001119104] ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences[E2CX4425YZ]
WOS研究方向Computer Science
语种英语
出版者SPRINGERNATURE
WOS记录号WOS:000867657800001
资助机构Sci-Tech Innovation 2030 -Major Project of Brain Science and Brain-inspired Intelligence Technology ; National Key R&D Program of China ; National Natural Science Foundation of China ; 13th Five-year Informatization Plan of Chinese Academy of Sciences ; Key Research Program of the Chinese Academy of Sciences ; Beijing Nova Program of Science and Technology ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences
源URL[http://ir.psych.ac.cn/handle/311026/43507]  
专题心理研究所_中国科学院心理健康重点实验室
心理研究所_中国科学院行为科学重点实验室
通讯作者Yan, Chao-Gan
作者单位1.Univ Chinese Acad Sci, Sino Danish Coll, Beijing, Peoples R China
2.Beijing Language & Culture Univ, Ctr Cognit Sci Language, Beijing, Peoples R China
3.Chinese Acad Sci, Magnet Resonance Imaging Res Ctr, Inst Psychol, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Psychol, Int Big Data Ctr Depress Res, Beijing, Peoples R China
5.Nathan S Kline Inst Psychiat Res, Orangeburg, NY USA
6.NYU Grossman Sch Med, Dept Child & Adolescent Psychiat, New York, NY USA
7.Univ Southern Calif, Keck Sch Med, Mark & Mary Stevens Inst Neuroimaging & Informat, Imaging Genet Ctr, Los Angeles, CA 90007 USA
8.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Radiol, Hangzhou, Zhejiang, Peoples R China
9.Fudan Univ, Huashan Hosp, Dept Neurosurg, Shanghai, Peoples R China
10.Sino Danish Ctr Educ & Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Bin,Li, Hui-Xian,Chang, Zhi-Kai,et al. A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples[J]. JOURNAL OF BIG DATA,2022,9(1):22.
APA Lu, Bin.,Li, Hui-Xian.,Chang, Zhi-Kai.,Li, Le.,Chen, Ning-Xuan.,...&Yan, Chao-Gan.(2022).A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples.JOURNAL OF BIG DATA,9(1),22.
MLA Lu, Bin,et al."A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples".JOURNAL OF BIG DATA 9.1(2022):22.

入库方式： OAI收割

来源：心理研究所

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