{"id":13265,"date":"2024-07-04T18:38:04","date_gmt":"2024-07-04T18:38:04","guid":{"rendered":"https:\/\/dailyai.com\/?p=13265"},"modified":"2024-07-04T23:28:31","modified_gmt":"2024-07-04T23:28:31","slug":"new-ai-model-successfully-identifies-alzheimers-disease-using-speech-analysis","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2024\/07\/new-ai-model-successfully-identifies-alzheimers-disease-using-speech-analysis\/","title":{"rendered":"Un nuovo sistema di intelligenza artificiale identifica con successo la malattia di Alzheimer utilizzando l'analisi del parlato"},"content":{"rendered":"

Analizzando i modelli di discorso, i ricercatori dell'Universit\u00e0 di Boston hanno sviluppato un sistema di intelligenza artificiale in grado di prevedere con una precisione di quasi 80% se una persona con un lieve deterioramento cognitivo svilupper\u00e0 la malattia di Alzheimer entro sei anni.<\/b><\/p>\n

Il <\/span>studio<\/span><\/a>pubblicato sulla rivista Alzheimer's & Dementia, utilizza l'intelligenza artificiale per estrarre informazioni diagnostiche preziose dalle valutazioni cognitive, accelerando la diagnosi di Alzheimer e, di conseguenza, il trattamento.\u00a0<\/span><\/p>\n

Il modello di intelligenza artificiale del team ha raggiunto un'accuratezza di 78,5% e una sensibilit\u00e0 di 81,1% nel predire la progressione dal decadimento cognitivo lieve (MCI) alla malattia di Alzheimer in un arco di tempo di sei anni. Questo risultato batte altri test tradizionali e non invasivi.<\/span><\/p>\n

Tuttavia, il sistema si basa esclusivamente su dati facilmente ottenibili: il discorso trascritto dalle valutazioni cognitive e le informazioni demografiche di base come l'et\u00e0, il sesso e il livello di istruzione. <\/span><\/p>\n

Le valutazioni cognitive, come il Boston Naming Test, prevedono che il medico parli con il paziente. L'audio di questi test viene spesso registrato per ulteriori analisi.\u00a0<\/span><\/p>\n

\"Volevamo prevedere cosa sarebbe successo nei prossimi sei anni e abbiamo scoperto che possiamo ragionevolmente fare questa previsione con una sicurezza e un'accuratezza relativamente buone\". <\/span>ha detto Ioannis (Yannis) Paschalidis<\/span><\/a>, direttore del BU Rafik B. Hariri Institute for Computing and Computational Science & Engineering e uno dei ricercatori principali dello studio.<\/span><\/p>\n

\"Se si pu\u00f2 prevedere cosa accadr\u00e0, si ha una maggiore opportunit\u00e0 e una finestra temporale per intervenire con i farmaci e cercare almeno di mantenere la stabilit\u00e0 della condizione e prevenire il passaggio a forme pi\u00f9 gravi di demenza\".<\/span><\/p>\n

Per saperne di pi\u00f9 sullo studio<\/h2>\n

Ecco come si \u00e8 svolto lo studio:<\/span><\/p>\n

    \n
  1. Il team di ricerca ha iniziato raccogliendo le registrazioni audio delle valutazioni cognitive di 166 partecipanti con diagnosi di decadimento cognitivo lieve (MCI). Hanno poi seguito questi individui per un periodo di sei anni per determinare chi progrediva verso la malattia di Alzheimer e chi rimaneva stabile.<\/span><\/li>\n
  2. Il team ha utilizzato una tecnologia avanzata di riconoscimento vocale per trascrivere le registrazioni audio e preparare i dati per l'analisi.\u00a0<\/span><\/li>\n
  3. Successivamente, i ricercatori hanno applicato sofisticate tecniche di elaborazione del linguaggio naturale per estrarre un'ampia gamma di caratteristiche e modelli linguistici che ritenevano potessero fungere da indicatori del rischio di Alzheimer.<\/span><\/li>\n
  4. Hanno poi utilizzato le caratteristiche del parlato e le informazioni demografiche per sviluppare diversi modelli di apprendimento automatico.<\/li>\n
  5. Questi modelli di intelligenza artificiale sono stati progettati per prevedere la probabilit\u00e0 che un determinato individuo passi da un lieve deterioramento cognitivo alla malattia di Alzheimer in base ai suoi modelli di linguaggio e alle sue caratteristiche personali.<\/span><\/li>\n
  6. I modelli hanno raggiunto un'accuratezza di 78,5% e una sensibilit\u00e0 di 81,1% nel prevedere quali partecipanti avrebbero sviluppato l'Alzheimer entro il periodo di studio di sei anni.<\/span><\/li>\n
  7. In un'analisi finale, il team di ricerca ha identificato i test cognitivi con il maggior potere predittivo del rischio di Alzheimer, come il Boston Naming Test, i test di somiglianza e la Wechsler Adult Intelligence Scale.<\/span><\/li>\n<\/ol>\n

    \"Il digitale \u00e8 il nuovo sangue\", ha dichiarato Rhoda Au, professore presso la Chobanian & Avedisian School of Medicine della BU e coautore dello studio.\u00a0<\/span><\/p>\n

    \"\u00c8 possibile raccoglierla, analizzarla per ci\u00f2 che \u00e8 noto oggi, archiviarla e rianalizzarla per qualsiasi novit\u00e0 emerga domani\".<\/span><\/p>\n

    Uno degli aspetti pi\u00f9 interessanti dello studio \u00e8 che alcune parti delle valutazioni cognitive erano particolarmente predittive del rischio futuro di Alzheimer.\u00a0<\/span><\/p>\n

    \"La nostra analisi ha rivelato che i subtest relativi a domande demografiche, il Boston Naming Test, i test di somiglianza e la Wechsler Adult Intelligence Scale sono emersi come le principali caratteristiche che guidano le prestazioni del nostro modello\", sottolineano i ricercatori.\u00a0<\/span><\/p>\n

    Questo potrebbe informare lo sviluppo di valutazioni cognitive pi\u00f9 mirate, semplificando ulteriormente il processo di screening.<\/span><\/p>\n

    Sebbene i risultati siano promettenti, i ricercatori ammettono la necessit\u00e0 di ulteriori convalide in popolazioni pi\u00f9 ampie e diversificate.\u00a0<\/span><\/p>\n

    Il riconoscimento vocale pu\u00f2 aprire le porte alla diagnosi precoce<\/h2>\n

    L'analisi del parlato si \u00e8 dimostrata una tecnica preziosa per prevedere il morbo di Alzheimer e altre malattie. <\/span><\/p>\n

    In un <\/span>Studio 2020<\/span><\/a> Analogamente allo studio dell'Universit\u00e0 di Boston, i ricercatori dell'Universit\u00e0 di Sheffield hanno dimostrato la capacit\u00e0 della loro IA di distinguere tra i partecipanti con malattia di Alzheimer o decadimento cognitivo lieve e quelli con disturbo cognitivo funzionale o controlli sani con un'accuratezza dell'86,7%.\u00a0<\/span><\/p>\n

    I ricercatori dei laboratori Klick hanno anche <\/span>sviluppato un modello di intelligenza artificiale<\/span><\/a> che pu\u00f2 rilevare il diabete di tipo 2 utilizzando brevi registrazioni vocali di soli 6-10 secondi. <\/span>Il diabete avanzato pu\u00f2 avere un impatto sulla voce attraverso il danneggiamento dei nervi, l'alterazione del flusso sanguigno e la secchezza delle fauci, con conseguenti alterazioni rilevabili.\u00a0<\/span><\/p>\n

    Lo studio ha analizzato 18.000 registrazioni per identificare le sottili differenze acustiche tra individui diabetici e non diabetici. <\/span><\/p>\n

    Se combinato con fattori come l'et\u00e0 e l'IMC, il modello ha raggiunto un'accuratezza massima di 89% per le donne e 86% per gli uomini.<\/span><\/p>\n

    L'insieme di questi studi dimostra che i test e i metodi diagnostici non invasivi supportati dall'intelligenza artificiale potrebbero portare a cure pi\u00f9 rapide ed efficaci, anche in assenza di medici specialisti e attrezzature.<\/p>","protected":false},"excerpt":{"rendered":"

    Analizzando i modelli di discorso, i ricercatori dell'Universit\u00e0 di Boston hanno sviluppato un sistema di intelligenza artificiale in grado di prevedere con una precisione di quasi 80% se una persona con un lieve deterioramento cognitivo svilupper\u00e0 la malattia di Alzheimer entro sei anni. Lo studio, pubblicato sulla rivista Alzheimer's & Dementia, utilizza l'intelligenza artificiale per estrarre informazioni diagnostiche preziose dalle valutazioni cognitive, accelerando la diagnosi di Alzheimer e, di conseguenza, il trattamento. Il modello di intelligenza artificiale del team ha raggiunto un'accuratezza di 78,5% e una sensibilit\u00e0 di 81,1% nel prevedere la progressione dal decadimento cognitivo lieve (MCI) alla malattia di Alzheimer in un arco di tempo di sei anni. Questo batte altri test tradizionali e non invasivi. Il sistema, tuttavia, si basa su<\/p>","protected":false},"author":2,"featured_media":13269,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[353,204,178],"class_list":["post-13265","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biology","tag-healthcare","tag-medicine"],"yoast_head":"\nNew AI system successfully identifies Alzheimer's disease using speech analysis | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/it\/2024\/07\/new-ai-model-successfully-identifies-alzheimers-disease-using-speech-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New AI system successfully identifies Alzheimer's disease using speech analysis | DailyAI\" \/>\n<meta property=\"og:description\" content=\"By analyzing speech patterns, researchers at Boston University have developed an AI system that can predict with nearly 80% accuracy whether someone with mild cognitive impairment will develop Alzheimer’s disease within six years. The study, published in the journal Alzheimer’s & Dementia, uses AI to extract valuable diagnostic information from cognitive assessments, accelerating Alzheimer\u2019s diagnosis and, in turn, treatment.\u00a0 The team’s AI model achieved an accuracy of 78.5% and a sensitivity of 81.1% in predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease within a six-year timeframe. This beats other traditional and non-invasive tests. 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