{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:48:52Z","timestamp":1787312932571,"version":"build-2736575974"},"reference-count":33,"publisher":"The Endocrine Society","issue":"4","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004885","name":"Ume\u00e5 University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004885","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Arnerska Fund"},{"name":"J C Kempe\u2019s Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>There is a considerable diagnostic delay in acromegaly, contributing to increased morbidity. Voice changes due to orofacial and laryngeal changes are common in acromegaly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Our aim was to explore the use of digital voice analysis as a biomarker for acromegaly using broad acoustic analysis and machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Voice recordings from patients with acromegaly and matched controls were collected using a mobile phone at Swedish university hospitals. Anthropometric and clinical data and the Voice Handicap Index (VHI) were assessed. Digital voice analysis of a sustained and stable vowel [a] resulted in 3274 parameters, which were used for training of machine learning models classifying the speaker as \u201cacromegaly\u201d or \u201ccontrol.\u201d The machine learning models were trained with 76% of the data and the remaining 24% was used to assess their performance. For comparison, voice recordings of 50 pairs of participants were assessed by 12 experienced endocrinologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We included 151 Swedish patients with acromegaly (13% biochemically active and 10% newly diagnosed) and 139 matched controls. The machine learning model identified patients with acromegaly more accurately (area under the receiver operating curve [ROC AUC] 0.84) than experienced endocrinologists (ROC AUC 0.69). Self-reported voice problems were more pronounced in patients with acromegaly than matched controls (median VHI 6 vs 2, P &amp;lt; .01) with higher prevalence of clinically significant voice handicap (VHI \u226520: 22.5% vs 3.6%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Digital voice analysis can identify patients with acromegaly from short voice recordings with high accuracy. Patients with acromegaly experience more voice disorders than matched controls.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgae689","type":"journal-article","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T23:16:40Z","timestamp":1727911000000},"page":"983-990","source":"Crossref","is-referenced-by-count":6,"title":["Digital Voice Analysis as a Biomarker of Acromegaly"],"prefix":"10.1210","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9501-6763","authenticated-orcid":false,"given":"Konstantina","family":"Vouzouneraki","sequence":"first","affiliation":[{"name":"Department of Public Health and Clinical Medicine, Ume\u00e5 University , SE-901 87 Ume\u00e5 ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-0934","authenticated-orcid":false,"given":"Fredrik","family":"Nyl\u00e9n","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, 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