干旱区生态环境知识资源中心(Arid): A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples

Arid

DOI	10.1186/s40537-022-00650-y
	A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples
	Lu, Bin; Li, Hui-Xian; Chang, Zhi-Kai; Li, Le; Chen, Ning-Xuan; Zhu, Zhi-Chen; Zhou, Hui-Xia; Li, Xue-Ying; Wang, Yu-Wei; Cui, Shi-Xian; Deng, Zhao-Yu; Fan, Zhen; Yang, Hong; Chen, Xiao; Thompson, Paul M.; Castellanos, Francisco Xavier; Yan, Chao-Gan
通讯作者	Yan, CG
来源期刊	JOURNAL OF BIG DATA
EISSN	2196-1115
出版年	2022
卷号	9 期号:1
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.
英文关键词	Alzheimer's disease Convolutional neural network Magnetic resonance brain imaging Sex differences Transfer learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000867657800001
WOS关键词	MRI ; SEX ; AGE ; REVEALS ; NETWORK ; CANCER
WOS类目	Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393345
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],2022,9(1).
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).
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).