Mobile detection of autism through machine learning on home video: A development and prospective validation study.

Journal: PLoS medicine
PMID: 30481180

Abstract

BACKGROUND: The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.

Authors

  • Qandeel Tariq
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Jena Daniels
    1Division of Systems Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA USA.
  • Jessey Nicole Schwartz
    Department of Pediatrics, Division of Systems Medicine, Stanford University, California, United States of America.
  • Peter Washington
    Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
  • Haik Kalantarian
    Department of Pediatrics (Systems Medicine), Stanford University, Stanford, California; Department of Biomedical Data Science, Stanford University, Stanford, California.
  • Dennis Paul Wall
    Department of Pediatrics, Division of Systems Medicine, Stanford University, California, United States of America.

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