Use of machine learning to shorten screening and diagnosis of autism
The process of diagnosing autism is complex, subjective, and often limited to only a segment of the population in need. With the recent rise in incidence to 1 in 88 children, the need for accurate and widely deployable methods for screening and diagnosis is substantial. Dennis Wall, associate professor of pathology and director of computational biology initiative at the Center for Biomedical Informatics at Harvard Medical School, has been working to address this problem and has discovered a highly accurate strategy that could significantly reduce the complexity and time of the diagnostic process.
Wall has been developing algorithms and associated deployment mechanisms to detect autism rapidly and with high accuracy. The algorithms are designed to work within a mobile architecture, combining a small set of questions and a short home video of the subject, to enable rapid online assessments. This procedure could reduce the time for autism diagnosis by nearly 95 percent, from hours to minutes, and could be easily integrated into routine child screening practices to enable a dramatic increase in reach to the population at risk.
“We believe this approach will make it possible for more children to be accurately diagnosed during the early critical period when behavioral therapies are most effective,” said Wall.
The first publication from this work has appeared online today, April 10, in Nature Translational Psychiatry. The article is available open access here.