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Autism


Can artificial intelligence detect autism doctors miss? US study wants to find out – TheStar

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It uses artificial intelligence to analyze toddlers' movements, eye positions, and facial expressions, among other things, to help predict who has …


Data Scientist - Remote Tech Jobs

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FORTA’S MISSION: As the rate of diagnosed autism cases increases, the ability to provide care has stagnated. Currently, autism support via Applied Behavior Analysis (ABA) therapy is difficult to access, thus leading to long wait lists. Forta is clearing the path to quality healthcare by delivering routes to the best…


Duke Awarded $12M Research Grant to Use Artificial Intelligence to Detect Autism

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The grant, from the National Institute of Child Health and Human Development, extends the Duke Autism Center of Excellence research program for an additional 5 years. Geraldine Dawson, Ph.D., director of the Duke Center for Autism and Brain Development and professor of psychiatry and behavioral sciences, will lead a team of researchers that includes Duke faculty from psychiatry, pediatrics, biostatistics and bioinformatics, computer and electrical engineering, and civil and environmental engineering. "We are thrilled to receive this award, which allows Duke to remain at the forefront of autism research," Dawson said. "Our goal is to use advanced computational techniques to develop better methods for autism screening that will reduce known disparities in access to early diagnosis and intervention." In a project led by Dawson and Guillermo Sapiro, Ph.D., professor of electrical and computer engineering, researchers will test a digital app, used by parents at home on a smart phone, to videotape young children's behavior and interactions with their caregivers.


Are children more likely to share mental health concerns with a robot?

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Socially assistive robots have previously demonstrated potential as a tool to improve the accessibility of care, the researchers explain in their paper. For instance, a 2020 study illustrated that robots may be helpful in assessing risk factors for autism spectrum disorder (ASD). "Robots have been used for various tasks -- and they've been shown to be effective in certain things because they have this physical embodiment, unlike a mobile phone or a virtual character or even videos," Prof. Gunes said. And despite the potential dangers of allowing a child too much time with an electronic device, working one-on-one with a robot is different from screentime, Prof. Gunes noted. "This is a physical interaction, right? It's not a video -- they're physically interacting with a physical entity," she said.


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Summary: Children on the autism spectrum may not always process bodily movements correctly, especially if they are distracted by something else. Noticing and understanding what it means when a person leans into a conversation or takes a step back and crosses their arms is a vital part of human communication. Researchers at the Del Monte Institute for Neuroscience at the University of Rochester have found that children with autism spectrum disorder may not always process body movements effectively, especially if they are distracted by something else. "Being able to read and respond to someone's body language is important in our daily interactions with others," said Emily Knight, M.D., Ph.D., clinical and postdoctoral fellow in Pediatrics and Neuroscience, is the first author of the study recently published in Molecular Autism. "Our findings suggest that when children with autism are distracted by something else, their brains process the movements of another person differently than their peers."


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According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.


Northwestern University Researchers Used Machine Learning To Identify Speech Patterns In Children With Autism That Were Consistent Between English And Cantonese

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According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.


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.