{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T17:56:41Z","timestamp":1788890201117,"version":"build-2803163510"},"reference-count":60,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer's Disease Reports"],"published-print":{"date-parts":[[2024,1,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Application of visual scoring scales for regional atrophy in Alzheimer\u2019s disease (AD) in clinical settings is limited by their high time cost and low intra\/inter-rater agreement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/adr-230105","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T13:18:33Z","timestamp":1715951913000},"page":"863-876","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated Scoring of Alzheimer\u2019s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation"],"prefix":"10.1177","volume":"8","author":[{"given":"Yeong Sim","family":"Choe","sequence":"first","affiliation":[{"name":"Research Institute, Neurophet Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina E.Y.","family":"Kim","sequence":"additional","affiliation":[{"name":"Research Institute, Neurophet Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Weon","family":"Kim","sequence":"additional","affiliation":[{"name":"Research Institute, Neurophet Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JeeYoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Eunpyeong St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunji","family":"Lee","sequence":"additional","affiliation":[{"name":"Research Institute, Neurophet Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Kyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Yeouido St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minho","family":"Lee","sequence":"additional","affiliation":[{"name":"Research Institute, Neurophet Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keun You","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Se-Hong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, St. Vincent\u2019s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea"},{"name":"Department of Psychiatry and Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eosu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Institute of Behavioral Science in Medicine, Brain Korea 21 FOUR Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of 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