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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\u2019s 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.<\/jats:p>","DOI":"10.1186\/s40537-022-00650-y","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T07:03:07Z","timestamp":1665644587000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["A practical Alzheimer\u2019s disease classifier via brain imaging-based deep learning on 85,721 samples"],"prefix":"10.1186","volume":"9","author":[{"given":"Bin","family":"Lu","sequence":"first","affiliation":[]},{"given":"Hui-Xian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhi-Kai","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Le","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ning-Xuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhi-Chen","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hui-Xia","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xue-Ying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yu-Wei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shi-Xian","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Zhao-Yu","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Paul M.","family":"Thompson","sequence":"additional","affiliation":[]},{"given":"Francisco Xavier","family":"Castellanos","sequence":"additional","affiliation":[]},{"given":"Chao-Gan","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"issue":"6","key":"650_CR1","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/S1474-4422(14)70090-0","volume":"13","author":"B Dubois","year":"2014","unstructured":"Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. 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