{"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\/pt\/2023\/10\/researchers-diagnose-diabetes-in-seconds-using-voice-recordings\/","title":{"rendered":"Os investigadores diagnosticam a diabetes em segundos utilizando grava\u00e7\u00f5es de voz"},"content":{"rendered":"

Os investigadores da empresa de investiga\u00e7\u00e3o na \u00e1rea da sa\u00fade Klick Labs desenvolveram um modelo de aprendizagem autom\u00e1tica que pode determinar a presen\u00e7a de diabetes tipo 2 a partir de uma breve grava\u00e7\u00e3o de voz de apenas 6 a 10 segundos.\u00a0<\/b><\/p>\n

O modelo apresenta uma taxa m\u00e1xima de exatid\u00e3o do teste de 89% para as mulheres e de 86% para os homens quando combinado com outras m\u00e9tricas como o \u00edndice de massa corporal (IMC).\u00a0<\/span><\/p>\n

\"A nossa investiga\u00e7\u00e3o destaca varia\u00e7\u00f5es vocais significativas entre indiv\u00edduos com e sem diabetes de tipo 2 e pode transformar a forma como a comunidade m\u00e9dica faz o rastreio da diabetes\", comentou Jaycee Kaufman, investigadora da Klick Labs.<\/span><\/p>\n

O m\u00e9todo, <\/span>publicado em Mayo Clinic Proceedings<\/span><\/a>requer um pequeno clip \u00e1udio gravado com um smartphone.\u00a0\u00a0<\/span><\/p>\n

\"Os m\u00e9todos actuais de dete\u00e7\u00e3o podem exigir muito tempo, desloca\u00e7\u00f5es e custos. A tecnologia de voz tem o potencial de eliminar totalmente estas barreiras\", acrescentou Kaufman.<\/span><\/p>\n

O estudo envolveu a an\u00e1lise de 18.000 grava\u00e7\u00f5es, com o objetivo de identificar caracter\u00edsticas ac\u00fasticas \u00fanicas que distinguem os indiv\u00edduos diab\u00e9ticos dos n\u00e3o diab\u00e9ticos, detectando diferen\u00e7as subtis de tom e intensidade impercept\u00edveis ao ouvido humano.<\/span><\/p>\n

A diabetes em fase avan\u00e7ada ou tardia pode ter impacto na voz ao causar danos nos nervos e prejudicar o fluxo sangu\u00edneo, levando a condi\u00e7\u00f5es como a neuropatia que afecta as cordas vocais. Isto pode resultar numa voz fraca, rouca ou cansada.\u00a0<\/span><\/p>\n

Al\u00e9m disso, a diabetes pode levar \u00e0 secura da boca devido \u00e0 redu\u00e7\u00e3o da produ\u00e7\u00e3o de saliva, contribuindo ainda mais para os problemas de voz.\u00a0<\/span><\/p>\n

Salientando o papel crescente da IA nos cuidados de sa\u00fade, os investigadores acreditam que esta tecnologia de voz pode ser alargada ao diagn\u00f3stico de outras condi\u00e7\u00f5es de sa\u00fade.<\/span><\/p>\n

Yan Fossat, vice-presidente da Klick Labs, partilhou: \"A nossa investiga\u00e7\u00e3o sublinha o enorme potencial da tecnologia de voz na identifica\u00e7\u00e3o da diabetes tipo 2 e de outros problemas de sa\u00fade. A tecnologia de voz pode revolucionar as pr\u00e1ticas de cuidados de sa\u00fade como uma ferramenta de rastreio digital acess\u00edvel e econ\u00f3mica.\"<\/span><\/p>\n

Mais informa\u00e7\u00f5es sobre o estudo<\/span><\/h2>\n

Este \u00e9 um estudo fascinante, mas ser\u00e1 que \u00e9 metodologicamente robusto? <\/span><\/p>\n

O tamanho da amostra \u00e9 relativamente pequeno e h\u00e1 pouca informa\u00e7\u00e3o sobre o grau de avan\u00e7o da diabetes no grupo de diab\u00e9ticos e pouca informa\u00e7\u00e3o sobre o grupo de controlo.<\/span><\/p>\n

No entanto, a previs\u00e3o das qualidades tonais subtis da fala em diab\u00e9ticos parece certamente poss\u00edvel para aqueles com formas avan\u00e7adas ou n\u00e3o controladas da doen\u00e7a.\u00a0<\/span><\/p>\n

Aqui est\u00e1 um olhar mais atento:<\/span><\/p>\n

    \n
  1. Objetivo:<\/b> O estudo tenta investigar se a an\u00e1lise da voz pode ajudar a pr\u00e9-selecionar ou monitorizar a diabetes mellitus tipo 2 (T2DM). Os investigadores concentraram-se em identificar diferen\u00e7as nas grava\u00e7\u00f5es de voz entre indiv\u00edduos com e sem DM2.<\/span><\/li>\n
  2. Metodologia:<\/b> 267 participantes, homens e mulheres, incluindo indiv\u00edduos n\u00e3o diab\u00e9ticos e com DM2, gravaram uma frase fixa v\u00e1rias vezes ao dia durante duas semanas, utilizando uma aplica\u00e7\u00e3o para smartphone. Este processo gerou mais de 18.000 grava\u00e7\u00f5es. A equipa extraiu 14 caracter\u00edsticas ac\u00fasticas diferentes destas grava\u00e7\u00f5es para analisar e comparar as vozes dos participantes n\u00e3o diab\u00e9ticos e com DMT2.<\/span><\/li>\n
  3. Conclus\u00f5es:<\/b> Os investigadores descobriram diferen\u00e7as significativas nas grava\u00e7\u00f5es de voz de homens e mulheres quando compararam os participantes n\u00e3o diab\u00e9ticos com os participantes com DM2. Identificaram caracter\u00edsticas de voz espec\u00edficas que previam com exatid\u00e3o o estado de T2DM, particularmente quando as combinavam com dados de idade e IMC. A precis\u00e3o m\u00e1xima do teste atingiu 89% para as mulheres e 86% para os homens.\u00a0<\/span><\/li>\n
  4. Implica\u00e7\u00f5es:<\/b> Estes resultados indicam que a an\u00e1lise da voz tem potencial como ferramenta de pr\u00e9-sele\u00e7\u00e3o ou de monitoriza\u00e7\u00e3o da DMT2. Este m\u00e9todo pode ser especialmente \u00fatil em comunidades remotas, proporcionando uma op\u00e7\u00e3o acess\u00edvel e n\u00e3o invasiva para a dete\u00e7\u00e3o precoce e a monitoriza\u00e7\u00e3o cont\u00ednua.<\/span><\/li>\n<\/ol>\n

    O conceito de diagnosticar uma doen\u00e7a atrav\u00e9s de uma grava\u00e7\u00e3o de voz pode parecer rebuscado, e \u00e9 essencial reconhecer que os melhores resultados s\u00f3 foram obtidos quando as grava\u00e7\u00f5es foram combinadas com o IMC. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Os investigadores da empresa de investiga\u00e7\u00e3o na \u00e1rea da sa\u00fade Klick Labs desenvolveram um modelo de aprendizagem autom\u00e1tica capaz de determinar a presen\u00e7a de diabetes tipo 2 a partir de uma breve grava\u00e7\u00e3o de voz de apenas 6 a 10 segundos. O modelo apresenta uma taxa m\u00e1xima de exatid\u00e3o do teste de 89% para as mulheres e de 86% para os homens, quando combinado com outras m\u00e9tricas como o \u00edndice de massa corporal (IMC). \"A nossa investiga\u00e7\u00e3o evidencia varia\u00e7\u00f5es vocais significativas entre indiv\u00edduos com e sem diabetes de tipo 2 e pode transformar a forma como a comunidade m\u00e9dica faz o rastreio da diabetes\", comentou Jaycee Kaufman, investigadora da Klick Labs. O m\u00e9todo, publicado na revista Mayo Clinic Proceedings, requer<\/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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