{"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\/da\/2023\/10\/researchers-diagnose-diabetes-in-seconds-using-voice-recordings\/","title":{"rendered":"Forskere diagnosticerer diabetes p\u00e5 f\u00e5 sekunder ved hj\u00e6lp af stemmeoptagelser"},"content":{"rendered":"

Forskere fra sundhedsforskningsfirmaet Klick Labs har udviklet en maskinl\u00e6ringsmodel, der kan bestemme tilstedev\u00e6relsen af type 2-diabetes ud fra en kort stemmeoptagelse p\u00e5 kun 6 til 10 sekunder.\u00a0<\/b><\/p>\n

Modellen viser en maksimal testn\u00f8jagtighed p\u00e5 89% for kvinder og 86% for m\u00e6nd, n\u00e5r den kombineres med andre parametre som body mass index (BMI).\u00a0<\/span><\/p>\n

\"Vores forskning fremh\u00e6ver betydelige vokale variationer mellem personer med og uden type 2-diabetes og kan \u00e6ndre den m\u00e5de, det medicinske samfund screener for diabetes p\u00e5\", siger Jaycee Kaufman, der er forsker ved Klick Labs.<\/span><\/p>\n

Metoden, <\/span>udgivet i Mayo Clinic Proceedings<\/span><\/a>kr\u00e6ver et kort smartphone-optaget lydklip.\u00a0\u00a0<\/span><\/p>\n

\"De nuv\u00e6rende opsporingsmetoder kan kr\u00e6ve en masse tid, rejser og omkostninger. Stemmeteknologi har potentiale til helt at fjerne disse barrierer,\" tilf\u00f8jer Kaufman.<\/span><\/p>\n

Unders\u00f8gelsen omfattede analyse af 18.000 optagelser med det form\u00e5l at identificere unikke akustiske tr\u00e6k, der adskiller diabetikere fra ikke-diabetikere, ved at opdage subtile forskelle i toneh\u00f8jde og intensitet, der er um\u00e6rkelige for det menneskelige \u00f8re.<\/span><\/p>\n

Senere eller fremskreden diabetes kan p\u00e5virke stemmen ved at for\u00e5rsage nerveskader og forringe blodgennemstr\u00f8mningen, hvilket f\u00f8rer til tilstande som neuropati, der p\u00e5virker stemmeb\u00e5ndene. Det kan resultere i en svag, h\u00e6s eller tr\u00e6t klingende stemme.\u00a0<\/span><\/p>\n

Derudover kan diabetes f\u00f8re til mundt\u00f8rhed p\u00e5 grund af nedsat spytproduktion, hvilket yderligere bidrager til stemmeproblemer.\u00a0<\/span><\/p>\n

Forskerne understreger AI's voksende rolle i sundhedsv\u00e6senet og mener, at denne stemmeteknologi kan udvides til at diagnosticere andre sundhedstilstande.<\/span><\/p>\n

Yan Fossat, vicepr\u00e6sident for Klick Labs, sagde: \"Vores forskning understreger stemmeteknologiens enorme potentiale til at identificere type 2-diabetes og andre sundhedsproblemer. Stemmeteknologi kan revolutionere sundhedspraksis som et tilg\u00e6ngeligt og overkommeligt digitalt screeningsv\u00e6rkt\u00f8j.\"<\/span><\/p>\n

Mere om unders\u00f8gelsen<\/span><\/h2>\n

Det er et fascinerende studie, men er det metodisk robust? <\/span><\/p>\n

Stikpr\u00f8vest\u00f8rrelsen er relativt lille, og der er begr\u00e6nset information om, hvor fremskreden diabetes er i diabetikergruppen, og kun lidt information om kontrolgruppen.<\/span><\/p>\n

Ikke desto mindre synes det at v\u00e6re muligt at forudsige de subtile tonale kvaliteter i tale hos diabetikere med fremskredne eller ukontrollerede former for sygdommen.\u00a0<\/span><\/p>\n

Her er et n\u00e6rmere kig:<\/span><\/p>\n

    \n
  1. M\u00e5ls\u00e6tning:<\/b> Studiet fors\u00f8ger at unders\u00f8ge, om stemmeanalyse kan hj\u00e6lpe med at forh\u00e5ndsscreene eller overv\u00e5ge type 2-diabetes mellitus (T2DM). Forskerne fokuserede p\u00e5 at identificere forskelle i stemmeoptagelser mellem personer med og uden T2DM.<\/span><\/li>\n
  2. Metodologi:<\/b> 267 deltagere, b\u00e5de m\u00e6nd og kvinder, herunder ikke-diabetikere og T2DM-personer, optog en fast s\u00e6tning flere gange dagligt i to uger ved hj\u00e6lp af en smartphone-app. Denne proces genererede over 18.000 optagelser. Holdet udtrak 14 forskellige akustiske tr\u00e6k fra disse optagelser for at analysere og sammenligne stemmerne hos deltagere uden diabetes og T2DM.<\/span><\/li>\n
  3. Resultater:<\/b> Forskerne opdagede betydelige forskelle i stemmeoptagelser fra b\u00e5de m\u00e6nd og kvinder, n\u00e5r de sammenlignede ikke-diabetiske deltagere med dem med T2DM. De identificerede specifikke stemmefunktioner, som pr\u00e6cist forudsagde T2DM-status, is\u00e6r n\u00e5r de blev kombineret med data om alder og BMI. Den maksimale testn\u00f8jagtighed n\u00e5ede 89% for kvinder og 86% for m\u00e6nd.\u00a0<\/span><\/li>\n
  4. Konsekvenser:<\/b> Disse resultater indikerer, at stemmeanalyse har potentiale som et v\u00e6rkt\u00f8j til forscreening eller overv\u00e5gning af T2DM. Denne metode kan is\u00e6r v\u00e6re nyttig i fjerntliggende samfund, hvor den giver en tilg\u00e6ngelig og ikke-invasiv mulighed for tidlig opsporing og l\u00f8bende overv\u00e5gning.<\/span><\/li>\n<\/ol>\n

    Konceptet med at diagnosticere sygdom gennem en stemmeoptagelse kan virke langt ude, og det er vigtigt at erkende, at de bedste resultater kun blev opn\u00e5et, n\u00e5r optagelserne blev kombineret med BMI. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere hos sundhedsforskningsfirmaet Klick Labs har udviklet en maskinl\u00e6ringsmodel, der kan bestemme tilstedev\u00e6relsen af type 2-diabetes ud fra en kort stemmeoptagelse p\u00e5 kun 6 til 10 sekunder. Modellen viser en maksimal testn\u00f8jagtighed p\u00e5 89% for kvinder og 86% for m\u00e6nd, n\u00e5r den kombineres med andre parametre som kropsmasseindeks (BMI). \"Vores forskning fremh\u00e6ver betydelige vokale variationer mellem personer med og uden type 2-diabetes og kan \u00e6ndre den m\u00e5de, det medicinske samfund screener for diabetes p\u00e5\", siger Jaycee Kaufman, der er forsker hos Klick Labs. Metoden, der er offentliggjort i Mayo Clinic Proceedings, kr\u00e6ver<\/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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