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Researchers Find Innovative Use of Machine Learning in Diagnosing Autism Spectrum Disorder (ASD)

Every year, millions of children between the age groups of five and fourteen years are diagnosed with a peculiar kind of neuro-biological development disorder called Autism Spectrum Disorder (ASD). In 2016, 62 million cases of ASD were reported globally. Despite impressive neuroscience innovations in the medical field in the last five decades, scientist have been unable to find a treatment for ASD. However, an early diagnosis of ASD can improve the lives of children suffering from autism. Now, researchers have found the benefits of machine learning algorithms in early detection of ASD and how different neuroscience techniques could be used to identify speech patterns among children with development problems related to communication, sensory processing and common social interactions.

The latest study was conducted by a group of experts in neuroscience, data curation, formal analysis and data visualization. The National Institutes of Health, Health and Medical Research Fund (Hong Kong: 02130846, PI: PW), Global Parent Child Resource Centre Limited and Dr Stanley Ho Medical Development Foundation provided grants to support this research on the role of machine learning in identifying the cross-linguistic patterns of speech prosodic differences in autism.

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Individuals with Autism Spectrum Disorder or Autism Spectrum Condition (ASD / ASC) show a wide range of characteristics that are linked to speech prosody. However, there was no common ground of research on ASD and how it varies among individuals who speak non-English languages. The researchers from the Northwestern University in the US collaborated with others based in Hong Kong to identify the differences between individuals who speak either English or Cantonese or both. The differences in the speech prosody among children with ASD was studied using supervised machine learning analytic approach to encode changes in loudness or pitch, emotions, and also underlying genetic influences on speech prosody.

Autism in US and How Machine Learning is Marching Forward

According to the Centers for Disease Control and Prevention, autism prevalence in the United States has risen significantly in recent years, from 1-in-150 in 2000 to 1-in-44 in 2022. Diagnosis rates among female and minority populations continue to lag, but novel telepsychiatry and automated diagnostic solutions are beginning to bridge the diagnosis gap.

While the U.S. is the global leader in ABA therapy, critical provider shortages still exist nationally, with more than half of all U.S. counties registering zero supervisory Board-certified Behavior Analysts, and 49 states not reaching the per capita benchmark supply of certified ABA providers. All 50 states now mandate ABA coverage for up to 40 hours per week which has led to private equity- and venture capital-backed ABA organization consolidation and growth to meet consumer demand that massively outpaces the current supply 10-to-1.

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Machine learning could be used to identify how speakers with ASD display varying pitch, in addition to having a slower speech rate and oddness of stress on syllables. By applying multivariate techniques of machine learning on acoustics features such as ‘rhythm’ and ‘intonation’, researchers were able to successfully classify individuals with “typical development” or TD from those who showed ASD features. ML Classification, based on Linear Support Vector Machines (SVMs), of TD versus ASD population was further used to ascertain the diagnostic characteristics of cognitive, neurological and speech impairments.

Why researchers used SVM in this ASD study?

The research paper fairly outlined the logic behind the use of support vector machines for ML classification of ASD features. It said, “the linear SVM is preferable to non-linear kernels because theoretically it is always possible to find a linear decision boundary that separates data, in spite of high data dimensionality and small sample size.”

Also, SVM is perfect for handling of high dimension data that are often linked with the various types of speech acoustic features linked to ASD.

Future of Neuroscience to Correct ASD

Machine learning will play a big role in the ASD and its management. From gene expression to neuro-imaging to fundamentally understanding a speaker’s progress as a TD individual, machine learning’s role transcends culture and hereditary.

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