{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:39:20Z","timestamp":1777941560530,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Shanghai National Science Foundation","doi-asserted-by":"publisher","award":["18ZR1405400 17ZR1427700"],"award-info":[{"award-number":["18ZR1405400 17ZR1427700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Mol Imaging"],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer\u2019s Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result:<\/jats:title>\n                    <jats:p>A total of 1103 ROIs were obtained. One hundred features were learned from ROIs using the DBN. The classification accuracy using linear, polynomial, and RBF kernels was 83.9%, 79.2%, and 86.6%, respectively. This method may be a powerful tool for personalized precision medicine in the population with prediction of early AD progression.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1536012119877285","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T08:19:41Z","timestamp":1569399581000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":42,"title":["Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images"],"prefix":"10.1177","volume":"18","author":[{"given":"Ting","family":"Shen","sequence":"first","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4948-3683","authenticated-orcid":false,"given":"Jiehui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaying","family":"Lu","sequence":"additional","affiliation":[{"name":"PET Center, Huashan Hospital, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuantao","family":"Zuo","sequence":"additional","affiliation":[{"name":"PET Center, Huashan Hospital, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Geriatric Institute of Chinese Medicine, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuangzhi","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 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