{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:26:46Z","timestamp":1788449206372,"version":"build-2803163510"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"6","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Key-Area Research and Development Program of Hunan Province","award":["2023SK2059"],"award-info":[{"award-number":["2023SK2059"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2025JJ50156, 2024JJ5271"],"award-info":[{"award-number":["2025JJ50156, 2024JJ5271"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project of Biomedical Engineering Fusion Laboratory in the Affiliated Jiangning Hospital of Nanjing Medical University","award":["JNYYZXKY202106"],"award-info":[{"award-number":["JNYYZXKY202106"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32201134, 32171366"],"award-info":[{"award-number":["32201134, 32171366"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2025-0028_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Accurate and timely swallowing ability screening is essential for effective prevention and treatment of dysphagia. Current clinical imaging methods are often radioactive or invasive, limiting their applicability for routine monitoring. This study aims to develop a smartphone-based intelligent system for real-time screening of swallowing ability using a wearable throat microphone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0028_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>First, a customized Android application was developed to collect, visualize, and analyze sounds related to swallowing from a wearable throat microphone. Second, a transfer learning model, YAMNet-S, was trained on 4,715\u00a0one-second audio segments of coughing, swallowing, and other noises, obtained from 215 healthy participants (Experiment 1). The trained model was then deployed on a smartphone to classify swallowing-related events in real time. Finally, the water swallow test (WST) was conducted by counting swallowing and coughing events within 30\u202fs from 15 simulated patients with self-induced voluntary coughs for clinical validation (Experiment\u00a02).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0028_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The mean accuracy of the trained model in swallowing and coughing events classification is 94.48 and 94.45\u202f%, respectively, and the difference of WST scores in system calculations and expert evaluation was 0.267 (out of\u00a05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0028_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This smartphone-based intelligent system has the potential to be a comfort, portability and user-friendliness tool for preliminary screening of dysphagia before VFSS\/FEES, especially on some situations with limited medical resources, such as community or home-based\u00a0care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2025-0028","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T13:03:42Z","timestamp":1752498222000},"page":"503-512","source":"Crossref","is-referenced-by-count":1,"title":["Smartphone-based intelligent system with a wearable throat microphone for real-time screening of swallowing ability"],"prefix":"10.1515","volume":"70","author":[{"given":"Ye","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electronics , 12568 Hunan Normal University , Changsha , Hunan , P.R. 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China"},{"name":"Institute of Interdisciplinary Studies , Hunan Normal University , Changsha , P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanjuan","family":"Fu","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Fusion Laboratory , Nanjing Jiangning Hospital (The Affiliated Jiangning Hospital of Nanjing Medical University) , Nanjing , P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronics , 12568 Hunan Normal University , Changsha , Hunan , P.R. China"},{"name":"Institute of Interdisciplinary Studies , Hunan Normal University , Changsha , P.R. China"},{"name":"Biomedical Engineering Fusion Laboratory , Nanjing Jiangning Hospital (The Affiliated Jiangning Hospital of Nanjing Medical University) , Nanjing , P.R. 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J\u00a0Adv Nurs 2016;72:2575\u201386. https:\/\/doi.org\/10.1111\/jan.13013.","DOI":"10.1111\/jan.13013"},{"key":"2025121816083920472_j_bmt-2025-0028_ref_004","doi-asserted-by":"crossref","unstructured":"Sarraf Shirazi, S, Buchel, C, Daun, R, Lenton, L, Moussavi, Z. Detection of swallows with silent aspiration using swallowing and breath sound analysis. Med Biol Eng Comput 2012;50:1261\u20138. https:\/\/doi.org\/10.1007\/s11517-012-0958-9.","DOI":"10.1007\/s11517-012-0958-9"},{"key":"2025121816083920472_j_bmt-2025-0028_ref_005","doi-asserted-by":"crossref","unstructured":"Yagi, N, Nagami, S, Lin, MK, Yabe, T, Itoda, M, Imai, T, et al.. A noninvasive swallowing measurement system using a combination of respiratory flow, swallowing sound, and laryngeal motion. 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Pervasive Mob Comput 2023;89:101754. https:\/\/doi.org\/10.1016\/j.pmcj.2023.101754.","DOI":"10.1016\/j.pmcj.2023.101754"},{"key":"2025121816083920472_j_bmt-2025-0028_ref_032","doi-asserted-by":"crossref","unstructured":"Zolnoori, M, Zolnour, A, Topaz, M. ADscreen: a speech processing-based screening system for automatic identification of patients with Alzheimer\u2019s disease and related dementia. Artif Intell Med 2023;143:102624. https:\/\/doi.org\/10.1016\/j.artmed.2023.102624.","DOI":"10.1016\/j.artmed.2023.102624"},{"key":"2025121816083920472_j_bmt-2025-0028_ref_033","doi-asserted-by":"crossref","unstructured":"Roy, A, Satija, U. A novel melspectrogram snippet representation\u00a0learning framework for severity detection of chronic obstructive pulmonary diseases. 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IEEE J Transl EngHealth Med 2015;3:1\u201310. https:\/\/doi.org\/10.1109\/jtehm.2015.2500562.","DOI":"10.1109\/JTEHM.2015.2500562"},{"key":"2025121816083920472_j_bmt-2025-0028_ref_037","doi-asserted-by":"crossref","unstructured":"Kuramoto, N, Ichimura, K, Jayatilake, D, Shimokakimoto, T, Hidaka, K, Suzuki, K. Deep learning-based swallowing monitor for realtime detection of swallow duration. In: 2020 42nd Annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 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