Diagnosing autism is a complex and subjective process. With the recent rise in incidence to 1 in 88 children, has been partially attributed to improving diagnostic measures but there is still a need for accurate and widely deployable methods for screening and diagnosis.
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 on this problem and has discovered a highly accurate strategy that could dramatically reduce the complexity and time of forming a diagnosis.
Wall has been developing a system to detect autism rapidly and with high accuracy. His system combines a small set of questions and a short home video of the subject, to enable rapid online assessments. This method could reduce the time for autism diagnosis by nearly 95 percent and could be integrated simply into routine child screening procedures and could reach the population at risk that have previously been short on diagnosis.
“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.
This research will be published April 10 online in Translational Psychiatry.
Current diagnostic measures can take up to three hours to complete and must be administered by a trained clinician. There can be a delay of more than a year between initial warning signs and diagnosis because of the waiting times to see a clinical professional who can administer the tests and deliver the formal diagnosis, Wall said.
Using machine learning techniques, an artificial intelligence method where machines are trained to make decisions, Wall and his team discovered that just seven questions were sufficient to diagnose autism with nearly 100 percent accuracy compared to the full 93-question exam.
They validated the accuracy of the seven question survey against answer sets from more than 1,600 individuals from the Simons Foundation and more than 300 individuals from the Boston Autism Consortium.
Wall believes these results have tremendous potential to move a substantial percentage of the effort into a mobilized electronic health framework with broad reach and applications.
“This approach is the first attempt to retrospectively analyze large data repositories to derive a highly accurate, but significantly abbreviated classification tool,” said Wall, who is also associate professor of pathology at Beth Israel Deaconess Medical Center. “This kind of rapid assessment should provide valuable contributions to the diagnostic process moving forward and help lead to faster screening and earlier treatment,” he said.
Wall has made a survey and video site that is currently available to the public for free to continue evaluating the effectiveness of the new shortened approach and is working on ways to mobilize the overall approach to expand its reach.