{"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\/fr\/2023\/10\/researchers-diagnose-diabetes-in-seconds-using-voice-recordings\/","title":{"rendered":"Des chercheurs diagnostiquent le diab\u00e8te en quelques secondes gr\u00e2ce \u00e0 des enregistrements vocaux"},"content":{"rendered":"

Des chercheurs de l'entreprise de recherche en soins de sant\u00e9 Klick Labs ont mis au point un mod\u00e8le d'apprentissage automatique capable de d\u00e9terminer la pr\u00e9sence d'un diab\u00e8te de type 2 \u00e0 partir d'un bref enregistrement vocal de 6 \u00e0 10 secondes seulement.\u00a0<\/b><\/p>\n

Le mod\u00e8le montre un taux de pr\u00e9cision maximal de 89% pour les femmes et de 86% pour les hommes lorsqu'il est combin\u00e9 \u00e0 d'autres param\u00e8tres comme l'indice de masse corporelle (IMC).\u00a0<\/span><\/p>\n

\"Notre recherche met en \u00e9vidence des variations vocales significatives entre les personnes atteintes ou non de diab\u00e8te de type 2 et pourrait transformer la mani\u00e8re dont la communaut\u00e9 m\u00e9dicale d\u00e9piste le diab\u00e8te\", a comment\u00e9 Jaycee Kaufman, chercheur \u00e0 Klick Labs.<\/span><\/p>\n

La m\u00e9thode, <\/span>publi\u00e9 dans Mayo Clinic Proceedings<\/span><\/a>L'inscription sur la liste des candidats \u00e0 l'\u00e9lection pr\u00e9sidentielle est subordonn\u00e9e \u00e0 l'enregistrement d'un court clip audio \u00e0 l'aide d'un t\u00e9l\u00e9phone intelligent.\u00a0\u00a0<\/span><\/p>\n

\"Les m\u00e9thodes actuelles de d\u00e9tection peuvent n\u00e9cessiter beaucoup de temps, de d\u00e9placements et d'argent. La technologie vocale a le potentiel d'\u00e9liminer compl\u00e8tement ces obstacles\", a ajout\u00e9 M. Kaufman.<\/span><\/p>\n

L'\u00e9tude a port\u00e9 sur l'analyse de 18 000 enregistrements, dans le but d'identifier les caract\u00e9ristiques acoustiques uniques qui permettent de distinguer les diab\u00e9tiques des non-diab\u00e9tiques, en d\u00e9tectant des diff\u00e9rences subtiles de hauteur et d'intensit\u00e9 imperceptibles \u00e0 l'oreille humaine.<\/span><\/p>\n

Un diab\u00e8te avanc\u00e9 peut avoir un impact sur la voix en provoquant des l\u00e9sions nerveuses et en r\u00e9duisant la circulation sanguine, ce qui entra\u00eene des pathologies telles que la neuropathie qui affecte les cordes vocales. Cela peut se traduire par une voix faible, rauque ou fatigu\u00e9e.\u00a0<\/span><\/p>\n

En outre, le diab\u00e8te peut entra\u00eener une s\u00e9cheresse buccale due \u00e0 une diminution de la production de salive, ce qui contribue \u00e0 aggraver les probl\u00e8mes de voix.\u00a0<\/span><\/p>\n

Soulignant le r\u00f4le croissant de l'IA dans les soins de sant\u00e9, les chercheurs estiment que cette technologie vocale pourrait \u00eatre \u00e9tendue au diagnostic d'autres probl\u00e8mes de sant\u00e9.<\/span><\/p>\n

Yan Fossat, vice-pr\u00e9sident de Klick Labs, a d\u00e9clar\u00e9 : \"Nos recherches soulignent l'\u00e9norme potentiel de la technologie vocale dans l'identification du diab\u00e8te de type 2 et d'autres probl\u00e8mes de sant\u00e9. La technologie vocale pourrait r\u00e9volutionner les pratiques de soins de sant\u00e9 en tant qu'outil de d\u00e9pistage num\u00e9rique accessible et abordable.\"<\/span><\/p>\n

En savoir plus sur l'\u00e9tude<\/span><\/h2>\n

Cette \u00e9tude est fascinante, mais est-elle solide sur le plan m\u00e9thodologique ? <\/span><\/p>\n

La taille de l'\u00e9chantillon est relativement petite, et il y a peu d'informations sur le degr\u00e9 d'avancement du diab\u00e8te dans le groupe diab\u00e9tique et peu d'informations sur le groupe de contr\u00f4le.<\/span><\/p>\n

N\u00e9anmoins, pr\u00e9dire les qualit\u00e9s tonales subtiles de la parole chez les diab\u00e9tiques semble certainement possible pour ceux qui pr\u00e9sentent des formes avanc\u00e9es ou non g\u00e9r\u00e9es de la maladie.\u00a0<\/span><\/p>\n

Voici un aper\u00e7u plus d\u00e9taill\u00e9 :<\/span><\/p>\n

    \n
  1. Objectif :<\/b> L'\u00e9tude tente de d\u00e9terminer si l'analyse de la voix pourrait contribuer au d\u00e9pistage ou au suivi du diab\u00e8te sucr\u00e9 de type 2 (DT2). Les chercheurs se sont attach\u00e9s \u00e0 identifier les diff\u00e9rences dans les enregistrements vocaux entre les personnes atteintes ou non de DT2.<\/span><\/li>\n
  2. M\u00e9thodologie :<\/b> 267 participants, hommes et femmes, y compris des personnes non diab\u00e9tiques et des personnes atteintes de DT2, ont enregistr\u00e9 une phrase fixe plusieurs fois par jour pendant deux semaines \u00e0 l'aide d'une application pour smartphone. Ce processus a g\u00e9n\u00e9r\u00e9 plus de 18 000 enregistrements. L'\u00e9quipe a extrait 14 caract\u00e9ristiques acoustiques diff\u00e9rentes de ces enregistrements afin d'analyser et de comparer les voix des participants non diab\u00e9tiques et des participants atteints de DT2.<\/span><\/li>\n
  3. Les r\u00e9sultats :<\/b> Les chercheurs ont d\u00e9couvert des diff\u00e9rences significatives dans les enregistrements vocaux des hommes et des femmes lorsqu'ils ont compar\u00e9 les participants non diab\u00e9tiques \u00e0 ceux atteints de DT2. Ils ont identifi\u00e9 des caract\u00e9ristiques vocales sp\u00e9cifiques qui permettaient de pr\u00e9dire avec pr\u00e9cision le statut de DT2, notamment en les combinant avec les donn\u00e9es relatives \u00e0 l'\u00e2ge et \u00e0 l'IMC. La pr\u00e9cision maximale des tests a atteint 89% pour les femmes et 86% pour les hommes.\u00a0<\/span><\/li>\n
  4. Implications :<\/b> Ces r\u00e9sultats indiquent que l'analyse de la voix pourrait servir d'outil de pr\u00e9s\u00e9lection ou de surveillance du DT2. Cette m\u00e9thode pourrait \u00eatre particuli\u00e8rement utile dans les communaut\u00e9s isol\u00e9es, en offrant une option accessible et non invasive pour le d\u00e9pistage pr\u00e9coce et le suivi continu.<\/span><\/li>\n<\/ol>\n

    L'id\u00e9e de diagnostiquer une maladie \u00e0 partir d'un enregistrement vocal peut sembler farfelue, et il est essentiel de reconna\u00eetre que les meilleurs r\u00e9sultats n'ont \u00e9t\u00e9 obtenus que lorsque les enregistrements ont \u00e9t\u00e9 combin\u00e9s \u00e0 l'IMC. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Des chercheurs de la soci\u00e9t\u00e9 de recherche en sant\u00e9 Klick Labs ont mis au point un mod\u00e8le d'apprentissage automatique capable de d\u00e9terminer la pr\u00e9sence d'un diab\u00e8te de type 2 \u00e0 partir d'un bref enregistrement vocal de 6 \u00e0 10 secondes seulement. Le mod\u00e8le pr\u00e9sente un taux de pr\u00e9cision maximal de 89% pour les femmes et de 86% pour les hommes lorsqu'il est associ\u00e9 \u00e0 d'autres mesures telles que l'indice de masse corporelle (IMC). \"Notre recherche met en \u00e9vidence des variations vocales significatives entre les individus atteints ou non de diab\u00e8te de type 2 et pourrait transformer la mani\u00e8re dont la communaut\u00e9 m\u00e9dicale d\u00e9piste le diab\u00e8te\", a comment\u00e9 Jaycee Kaufman, chercheur chez Klick Labs. La m\u00e9thode, publi\u00e9e dans Mayo Clinic Proceedings, n\u00e9cessite<\/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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