{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:31:22Z","timestamp":1788165082666,"version":"build-2803163510"},"reference-count":48,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020-\ub370\uc774\ud130-\uc704102"],"award-info":[{"award-number":["2020-\ub370\uc774\ud130-\uc704102"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["S2989403"],"award-info":[{"award-number":["S2989403"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Speech abnormalities are recognized as early indicators of Alzheimer's disease (AD) and mild cognitive impairment (MCI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To determine whether deep-learning models trained on mel-spectrograms of brief speech tasks can (i) discriminate individuals with MCI and AD from cognitively normal controls (NC) and (ii) estimate cognitive status with clinically useful accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Speech from 594 participants (185 NC, 231 MCI, 178 AD) was recorded through a mobile application that included 11 cognitive-linguistic tasks. Audio was converted into mel-spectrogram images and processed using a VGG16-based deep-learning model with transfer learning and fine-tuning of block 5. Task-specific feature vectors were extracted, concatenated, and used to train a deep neural network. The dataset was split into training, validation, and test sets (3:1:1), and five-split cross-validation was performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model demonstrated an overall accuracy of 72.4% in classifying NC from the abnormal group (MCI and AD), with sensitivity and specificity of 72.5% and 72.2%, respectively, a balanced accuracy of 72.4%, and an AUC of 0.997. In binary classifications, the model achieved 82.9% accuracy (balanced accuracy 82.9%, AUC 0.992) for NC versus AD, 70.7% accuracy (balanced accuracy 70.3%, AUC 0.956) for NC versus MCI, and 77.5% accuracy (balanced accuracy 78.9%, AUC 0.889) for MCI versus AD. Tasks such as serial subtraction, storytelling, and picture description contributed most to classification performance, indicating their effectiveness in capturing cognitive deficits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Mel-spectrogram-based deep-learning analysis of speech shows promise as a rapid, non-invasive, and language-independent screening tool for early cognitive impairment, with potential advantages over traditional assessments such as the Mini-Mental State Examination.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251401202","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:55:01Z","timestamp":1765551301000},"page":"928-939","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep-learning analysis of speech using mel-spectrograms for the assessment of mild cognitive impairment and Alzheimer's disease"],"prefix":"10.1177","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6463-2857","authenticated-orcid":false,"given":"Yun Ho","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Neurology, Incheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8866-5187","authenticated-orcid":false,"given":"Hyungjun","family":"Kim","sequence":"additional","affiliation":[{"name":"Aible Therapeutics, Co., Ltd., Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhun","family":"Hong","sequence":"additional","affiliation":[{"name":"Aible Therapeutics, Co., Ltd., Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaneun","family":"Baek","sequence":"additional","affiliation":[{"name":"Aible 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