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Digital phenotyping could help detect autism

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Researchers have developed a screening tool for autism that uses computer vision and machine learning to analyze autism-related behaviors — but greater reliability and robust validation will be needed if such tools are to be used in primary care settings.

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Acknowledgements

This research was supported by a grant from the National Institute of Mental Health (R01MH08187), a grant from the National Institute of Child Health and Human Development (K23HD099275) and by the Simons Foundation Autism Research Initiative (624965, 977910).

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Authors and Affiliations

  1. Department of Psychiatry, Division of Child Psychiatry, UCLA David Geffen School of Medicine, Los Angeles, CA, USA

    Catherine Lord & Rujuta B. Wilson

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  1. Catherine Lord
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  2. Rujuta B. Wilson
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Correspondence to Catherine Lord.

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Competing interests

C.L. reports royalties from Western Psychological Services for diagnostic instruments, including the Autism Diagnostic Observation Schedule (ADOS), the Autism Diagnostic Interview-Revised (ADI-R) and the Social Communication Questionnaire (SCQ). She is also on the scientific advisory boards or projects for Tilray, Roche, Gateway, Springtide and Greenwich Biosciences. R.B.W. declares no competing interests.

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Lord, C., Wilson, R.B. Digital phenotyping could help detect autism. Nat Med 29, 2412–2413 (2023). https://doi.org/10.1038/s41591-023-02557-4

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