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AI Detects Autism Speech Patterns Across Different Languages - AI Summary

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Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages. The data used to train the algorithm were recordings of English- and Cantonese-speaking young people with and without autism telling their own version of the story depicted in a wordless children's picture book called "Frog, Where Are You?" "Using this method, we were able to identify features of speech that can predict the diagnosis of autism," said Lau, a postdoctoral researcher working with Losh in the Roxelyn and Richard Pepper Department of Communication Sciences and Disorders at Northwestern. Finally, the results of the study could inform efforts to identify and understand the role of specific genes and brain processing mechanisms implicated in genetic susceptibility to autism, the authors said. Using a supervised machine-learning analytic approach, we examined acoustic features relevant to rhythmic and intonational aspects of prosody derived from narrative samples elicited in English and Cantonese, two typologically and prosodically distinct languages. Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages.


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A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies.


AI Detects Autism Speech Patterns Across Different Languages - Neuroscience News

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Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages. A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism.


AI detects autism speech patterns across different languages

#artificialintelligence

A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism. The data used to train the algorithm were recordings of English- and Cantonese-speaking young people with and without autism telling their own version of the story depicted in a wordless children's picture book called "Frog, Where Are You?" The results were published in the journal PLOS One on June 8, 2022.


AI detects autism speech patterns across different languages

#artificialintelligence

Northwestern University researchers used machine learning — a branch of artificial intelligence — to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition.


Algorithm uses brain 'fingerprints' to detect autism - Futurity

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You are free to share this article under the Attribution 4.0 International license. A new algorithm may help discern if someone has autism by looking at brain scans. The algorithm also successfully predicts severity of autism symptoms in individual people. With further honing, the algorithm could lead to earlier diagnoses, more targeted therapies, and broadened understanding of autism's origins in the brain. "…our AI-driven brain'fingerprinting' model could potentially be a powerful new tool in advancing diagnosis and treatment."


Technical Perspective: Personalized Recommendation of PoIs to People with Autism

Communications of the ACM

Recommender systems are among the most pervasive machine learning applications on the Internet. Social media, audio and video streaming, news, and e-commerce are all heavily driven by the data-intensive personalization they enable, leveraging information drawn from the behavior of large user bases to offer a myriad of recommendation services. Point of Interest (PoI) recommendation is the task of recommending locations (business, cultural sites, natural areas) for a user to visit. This is a well-established sub-field within recommender systems, and as a domain of application, it provides a good introduction to the challenges of applying personalized recommendation in practical contexts. An effective PoI recommender must consider a user's interests and preferences, as in any personalized system, but also practical aspects of travel: weather, congestion, hours of operation, seasonality, to name a few.


Has AI found a treatment for Fragile X?

ZDNet

Drug discovery and trialling is usually a long and tedious process. What if AI can substantially speed it up? That's the premise of Quris, an artificial intelligence company hoping to disrupt the pharmaceutical arena. The company just announced the final close of $28 million in seed funding to support its clinical prediction platform. The platform launched last year and has made quick progress: Quris is prepping a drug that addresses Fragile X syndrome (FXS), a common cause of Autism spectrum disorders, for clinical testing in 2022.


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Despite being frequent, this disorder is quite challenging to diagnose before the age of five. Scientists at the University of Geneva now have come up with an AI algorithm based on the automated analysis of videos, making it possible to study children's non-verbal communication in an anonymous and standardized manner. Nada Kojovic, a researcher in Marie Schaer's team and first author of the study, said, "Autism is characterized by a non-verbal communication that differs from that of a typically- developing child. It differs on several points, such as the difficulty in establishing eye contact, smiling, pointing to objects or the way they are interested in what surrounds them." "This is why we designed an algorithm using artificial intelligence that analyses the children's movements on video and identifies whether or not they are characteristic of autism spectrum disorder."


Using AI, scientists developed a device to detect autism

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Despite being frequent, this disorder is quite challenging to diagnose before the age of five. Scientists at the University of Geneva now have come up with an AI algorithm based on the automated analysis of videos, making it possible to study children's non-verbal communication in an anonymous and standardized manner. Nada Kojovic, a researcher in Marie Schaer's team and first author of the study, said, "Autism is characterized by a non-verbal communication that differs from that of a typically- developing child. It differs on several points, such as the difficulty in establishing eye contact, smiling, pointing to objects or the way they are interested in what surrounds them." "This is why we designed an algorithm using artificial intelligence that analyses the children's movements on video and identifies whether or not they are characteristic of autism spectrum disorder."