According to researchers, standard approaches to diagnose ASD assess up to 100 behaviors and take several hours to complete, which leads to a clinical backlog that can be detrimental for those ultimately diagnosed with autism.
“Behavioral interventions for ASD are most impactful when administered by or before 5 years of age; however, the diagnostic bottleneck that families face severely limits the impact of therapeutic interventions. Scalable measures are necessary to alleviate these bottlenecks, reduce waiting times for access to therapy, and reach underserved populations in need,” Qandeel Tariq, a data scientist within the department of pediatrics at Stanford University, and colleagues wrote.
Tariq and colleagues applied eight machine learning models to 116 home videos of children with autism (mean age, 4 years 10 months) and 46 videos of typically developing children (mean age, 2 years 11 months). The videos were 2 minutes long. Nonexpert raters needed 4 minutes to measure 30 behavioral features.
Researchers found that the five-feature logistic regression classifier — which used medical records generated through the administration of ADOS Module 2 — yielded the highest accuracy (area under the curve = 92%; 95% CI, 88-97) across all ages tested. A prospectively collected independent validation set of 66 videos, half featuring children with ASD, achieved lower but comparable accuracy (AUC = 89%; 95% CI, 81-95). Logistic regression to the 162-video-feature matrix to construct an eight-feature model achieved 0.93 AUC on a held-out test set and 0.86 AUC on the validation set of 66 videos. Other models — alternating decision trees, logistic regression, linear support vector machine, logistic regression, radial kernel, and support vector machine also performed well, but with less accuracy than the five-feature logistic regression classifier.
Home videos 2 minutes long were used to diagnose autism spectrum disorder in children, according to findings recently published in PLoS Medicine.
Source:Shutterstock
“Such a process could streamline autism diagnosis to enable earlier detection and earlier access to therapy that has the highest impact during earlier windows of social development. Further, this approach could help to reduce the geographic and financial burdens associated with access to diagnostic resources and provide more equal opportunity to underserved populations, including those in developing countries,” Tariq and colleagues wrote.
They added future studies should determine the most viable method of crowdsourcing video acquisition and feature tagging. In addition, trials with larger cohorts at various stages of autism diagnosis and developmental delay spectrums are needed to further explore the home video-machine learning tool’s full potential as a diagnostic tool, they added. – by Janel Miller
Disclosures: Tariq reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.
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