{"id":6648,"date":"2023-10-21T19:52:58","date_gmt":"2023-10-21T19:52:58","guid":{"rendered":"https:\/\/dailyai.com\/?p=6648"},"modified":"2023-10-21T20:03:47","modified_gmt":"2023-10-21T20:03:47","slug":"researchers-diagnose-diabetes-in-seconds-using-voice-recordings","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/10\/researchers-diagnose-diabetes-in-seconds-using-voice-recordings\/","title":{"rendered":"Forskere diagnostiserer diabetes p\u00e5 sekunder ved hjelp av stemmeopptak"},"content":{"rendered":"

Forskere ved helseforskningsselskapet Klick Labs har utviklet en maskinl\u00e6ringsmodell som kan avgj\u00f8re om det foreligger type 2-diabetes ut fra et kort stemmeopptak p\u00e5 bare 6 til 10 sekunder.\u00a0<\/b><\/p>\n

Modellen viser en maksimal testn\u00f8yaktighet p\u00e5 89% for kvinner og 86% for menn n\u00e5r den kombineres med andre m\u00e5leparametere som kroppsmasseindeks (BMI).\u00a0<\/span><\/p>\n

\"Forskningen v\u00e5r belyser betydelige vokale variasjoner mellom personer med og uten type 2-diabetes, og kan endre hvordan det medisinske samfunnet screener for diabetes\", kommenterer Jaycee Kaufman, forsker ved Klick Labs.<\/span><\/p>\n

Metoden, <\/span>publisert i Mayo Clinic Proceedings<\/span><\/a>krever et kort lydklipp som er tatt opp med smarttelefon.\u00a0\u00a0<\/span><\/p>\n

\"Dagens metoder for \u00e5 oppdage slike tilfeller kan kreve mye tid, reisevirksomhet og kostnader. Stemmeteknologi har potensial til \u00e5 fjerne disse hindringene helt og holdent\", legger Kaufman til.<\/span><\/p>\n

Studien omfattet analyse av 18 000 opptak, og m\u00e5let var \u00e5 identifisere unike akustiske trekk som skiller diabetikere fra ikke-diabetikere, ved \u00e5 oppdage subtile forskjeller i toneh\u00f8yde og intensitet som er umerkelige for det menneskelige \u00f8ret.<\/span><\/p>\n

P\u00e5 et senere stadium eller ved fremskreden diabetes kan stemmen p\u00e5virkes av nerveskader og nedsatt blodgjennomstr\u00f8mning, noe som kan f\u00f8re til tilstander som nevropati som p\u00e5virker stemmeb\u00e5ndene. Dette kan resultere i en svak, hes eller sliten stemme.\u00a0<\/span><\/p>\n

I tillegg kan diabetes f\u00f8re til munnt\u00f8rrhet p\u00e5 grunn av redusert spyttproduksjon, noe som bidrar ytterligere til stemmeproblemer.\u00a0<\/span><\/p>\n

Forskerne understreker at kunstig intelligens spiller en stadig viktigere rolle i helsevesenet, og mener at denne taleteknologien kan utvides til \u00e5 diagnostisere andre helsetilstander.<\/span><\/p>\n

Yan Fossat, visepresident i Klick Labs, sier: \"Forskningen v\u00e5r understreker det enorme potensialet stemmeteknologi har n\u00e5r det gjelder \u00e5 identifisere type 2-diabetes og andre helsetilstander. Stemmeteknologi kan revolusjonere helsevesenet som et tilgjengelig og rimelig digitalt screeningverkt\u00f8y.\"<\/span><\/p>\n

Mer om studien<\/span><\/h2>\n

Dette er en fascinerende studie, men er den metodisk robust? <\/span><\/p>\n

Utvalget er relativt lite, og det finnes begrenset informasjon om hvor fremskreden diabetes er i diabetikergruppen og lite informasjon om kontrollgruppen.<\/span><\/p>\n

Likevel ser det ut til at det er mulig \u00e5 forutsi de subtile tonale kvalitetene i tale hos diabetikere med en fremskreden eller ukontrollert form av sykdommen.\u00a0<\/span><\/p>\n

Her er en n\u00e6rmere titt:<\/span><\/p>\n

    \n
  1. M\u00e5lsetting:<\/b> Studien fors\u00f8ker \u00e5 unders\u00f8ke om stemmeanalyse kan bidra til \u00e5 forh\u00e5ndsscreene eller overv\u00e5ke diabetes mellitus type 2 (T2DM). Forskerne fokuserte p\u00e5 \u00e5 identifisere forskjeller i stemmeopptak mellom personer med og uten T2DM.<\/span><\/li>\n
  2. Metodikk:<\/b> 267 deltakere, b\u00e5de menn og kvinner, inkludert personer uten diabetes og T2DM, spilte inn en fast frase flere ganger daglig i to uker ved hjelp av en smarttelefonapp. Denne prosessen genererte over 18 000 opptak. Teamet trakk ut 14 ulike akustiske egenskaper fra disse opptakene for \u00e5 analysere og sammenligne stemmene til deltakere uten diabetes og T2DM.<\/span><\/li>\n
  3. Funn:<\/b> Forskerne oppdaget betydelige forskjeller i stemmeopptakene til b\u00e5de menn og kvinner n\u00e5r de sammenlignet deltakere uten diabetes med deltakere med T2DM. De identifiserte spesifikke stemmekjennetegn som n\u00f8yaktig kunne forutsi T2DM-status, s\u00e6rlig n\u00e5r de ble kombinert med alder og BMI-data. Maksimal testn\u00f8yaktighet n\u00e5dde 89% for kvinner og 86% for menn.\u00a0<\/span><\/li>\n
  4. Konsekvenser:<\/b> Disse resultatene tyder p\u00e5 at stemmeanalyse har potensial som et verkt\u00f8y for forh\u00e5ndsscreening eller overv\u00e5king av T2DM. Denne metoden kan v\u00e6re spesielt nyttig i avsidesliggende lokalsamfunn, der den kan v\u00e6re et tilgjengelig og ikke-invasivt alternativ for tidlig oppdagelse og l\u00f8pende overv\u00e5king.<\/span><\/li>\n<\/ol>\n

    Konseptet med \u00e5 diagnostisere sykdom ved hjelp av et stemmeopptak kan virke s\u00f8kt, og det er viktig \u00e5 v\u00e6re klar over at de beste resultatene bare ble oppn\u00e5dd n\u00e5r opptakene ble kombinert med BMI. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere ved helseforskningsselskapet Klick Labs har utviklet en maskinl\u00e6ringsmodell som kan avgj\u00f8re om det foreligger type 2-diabetes ut fra et kort stemmeopptak p\u00e5 bare 6 til 10 sekunder. Modellen viser en maksimal testn\u00f8yaktighet p\u00e5 89% for kvinner og 86% for menn n\u00e5r den kombineres med andre m\u00e5leparametere som kroppsmasseindeks (BMI). \"Forskningen v\u00e5r viser at det er betydelige vokale variasjoner mellom personer med og uten type 2-diabetes, og kan endre hvordan det medisinske samfunnet screener for diabetes\", kommenterer Jaycee Kaufman, forsker ved Klick Labs. Metoden, som er publisert i Mayo Clinic Proceedings, krever<\/p>","protected":false},"author":2,"featured_media":6649,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[203,105,101],"class_list":["post-6648","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biotech","tag-machine-learning","tag-medtech"],"yoast_head":"\nResearchers diagnose diabetes in seconds using voice recordings | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers at healthcare research company Klick Labs have developed a machine learning model that can determine the presence of Type 2 diabetes from a brief voice recording of just 6 to 10 seconds.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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