{"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\/es\/2023\/10\/researchers-diagnose-diabetes-in-seconds-using-voice-recordings\/","title":{"rendered":"Los investigadores diagnostican la diabetes en segundos mediante grabaciones de voz"},"content":{"rendered":"

Investigadores de la empresa de investigaci\u00f3n sanitaria Klick Labs han desarrollado un modelo de aprendizaje autom\u00e1tico capaz de determinar la presencia de diabetes de tipo 2 a partir de una breve grabaci\u00f3n de voz de s\u00f3lo 6 a 10 segundos.\u00a0<\/b><\/p>\n

El modelo muestra una tasa m\u00e1xima de precisi\u00f3n de la prueba de 89% para las mujeres y 86% para los hombres cuando se combina con otras m\u00e9tricas como el \u00edndice de masa corporal (IMC).\u00a0<\/span><\/p>\n

\"Nuestra investigaci\u00f3n pone de relieve variaciones vocales significativas entre individuos con y sin diabetes de tipo 2 y podr\u00eda transformar la forma en que la comunidad m\u00e9dica realiza las pruebas de detecci\u00f3n de la diabetes\", coment\u00f3 Jaycee Kaufman, investigadora cient\u00edfica de Klick Labs.<\/span><\/p>\n

El m\u00e9todo, <\/span>publicado en Mayo Clinic Proceedings<\/span><\/a>requiere un breve clip de audio grabado con un smartphone.\u00a0\u00a0<\/span><\/p>\n

\"Los m\u00e9todos actuales de detecci\u00f3n pueden requerir mucho tiempo, desplazamientos y costes. La tecnolog\u00eda vocal tiene el potencial de eliminar por completo estas barreras\", a\u00f1adi\u00f3 Kaufman.<\/span><\/p>\n

El estudio incluy\u00f3 el an\u00e1lisis de 18.000 grabaciones, con el objetivo de identificar caracter\u00edsticas ac\u00fasticas \u00fanicas que distinguieran a los individuos diab\u00e9ticos de los no diab\u00e9ticos, detectando sutiles diferencias de tono e intensidad imperceptibles para el o\u00eddo humano.<\/span><\/p>\n

La diabetes en fase avanzada puede afectar a la voz al provocar da\u00f1os en los nervios y alterar el flujo sangu\u00edneo, dando lugar a enfermedades como la neuropat\u00eda, que afecta a las cuerdas vocales. El resultado puede ser una voz d\u00e9bil, ronca o cansada.\u00a0<\/span><\/p>\n

Adem\u00e1s, la diabetes puede provocar sequedad de boca debido a una menor producci\u00f3n de saliva, lo que contribuye a\u00fan m\u00e1s a los problemas de voz.\u00a0<\/span><\/p>\n

Destacando el creciente papel de la IA en la atenci\u00f3n sanitaria, los investigadores creen que esta tecnolog\u00eda de voz podr\u00eda ampliarse para diagnosticar otras afecciones.<\/span><\/p>\n

Yan Fossat, Vicepresidente de Klick Labs, ha declarado: \"Nuestra investigaci\u00f3n subraya el enorme potencial de la tecnolog\u00eda de voz para identificar la diabetes de tipo 2 y otros problemas de salud. La tecnolog\u00eda de voz podr\u00eda revolucionar las pr\u00e1cticas sanitarias como herramienta de detecci\u00f3n digital accesible y asequible.\"<\/span><\/p>\n

M\u00e1s informaci\u00f3n sobre el estudio<\/span><\/h2>\n

Se trata de un estudio fascinante, pero \u00bfes metodol\u00f3gicamente s\u00f3lido? <\/span><\/p>\n

El tama\u00f1o de la muestra es relativamente peque\u00f1o, y hay poca informaci\u00f3n sobre lo avanzada que est\u00e1 la diabetes en el grupo de diab\u00e9ticos y poca informaci\u00f3n sobre el grupo de control.<\/span><\/p>\n

No obstante, predecir las sutiles cualidades tonales del habla en los diab\u00e9ticos parece ciertamente posible para aquellos con formas avanzadas o no controladas de la enfermedad.\u00a0<\/span><\/p>\n

He aqu\u00ed un vistazo m\u00e1s de cerca:<\/span><\/p>\n

    \n
  1. Objetivo:<\/b> El estudio trata de investigar si el an\u00e1lisis de la voz podr\u00eda ayudar a preseleccionar o controlar la diabetes mellitus tipo 2 (DMT2). Los investigadores se centraron en identificar diferencias en las grabaciones de voz entre individuos con y sin DMT2.<\/span><\/li>\n
  2. Metodolog\u00eda:<\/b> 267 participantes, hombres y mujeres, entre ellos personas no diab\u00e9ticas y con DMT2, grabaron una frase fija varias veces al d\u00eda durante dos semanas mediante una aplicaci\u00f3n para smartphone. Este proceso gener\u00f3 m\u00e1s de 18.000 grabaciones. El equipo extrajo 14 caracter\u00edsticas ac\u00fasticas diferentes de estas grabaciones para analizar y comparar las voces de los participantes no diab\u00e9ticos y con DMT2.<\/span><\/li>\n
  3. Hallazgos:<\/b> Los investigadores descubrieron diferencias significativas en las grabaciones de voz de hombres y mujeres al comparar a los participantes no diab\u00e9ticos con los que padec\u00edan DMT2. Identificaron caracter\u00edsticas espec\u00edficas de la voz que predec\u00edan con exactitud el estado de DMT2, sobre todo al combinarlas con datos de edad e IMC. Las precisiones m\u00e1ximas de las pruebas alcanzaron 89% en el caso de las mujeres y 86% en el de los hombres.\u00a0<\/span><\/li>\n
  4. Implicaciones:<\/b> Estos resultados indican que el an\u00e1lisis de la voz tiene potencial como herramienta de preselecci\u00f3n o seguimiento de la DMT2. Este m\u00e9todo podr\u00eda ser especialmente \u00fatil en comunidades remotas, ya que ofrece una opci\u00f3n accesible y no invasiva para la detecci\u00f3n precoz y el seguimiento continuo.<\/span><\/li>\n<\/ol>\n

    El concepto de diagnosticar enfermedades mediante una grabaci\u00f3n de voz puede parecer descabellado, y es esencial reconocer que los mejores resultados s\u00f3lo se obtuvieron cuando las grabaciones se combinaron con el IMC. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Investigadores de la empresa de investigaci\u00f3n sanitaria Klick Labs han desarrollado un modelo de aprendizaje autom\u00e1tico capaz de determinar la presencia de diabetes de tipo 2 a partir de una breve grabaci\u00f3n de voz de tan solo 6 a 10 segundos. El modelo muestra una tasa de precisi\u00f3n m\u00e1xima en la prueba de 89% para las mujeres y 86% para los hombres cuando se combina con otras m\u00e9tricas como el \u00edndice de masa corporal (IMC). \"Nuestra investigaci\u00f3n pone de relieve variaciones vocales significativas entre las personas con y sin diabetes de tipo 2 y podr\u00eda transformar la forma en que la comunidad m\u00e9dica realiza las pruebas de detecci\u00f3n de la diabetes\", coment\u00f3 Jaycee Kaufman, cient\u00edfico investigador de Klick Labs. El m\u00e9todo, publicado en Mayo Clinic Proceedings, requiere<\/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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