By Timothy Boyer emaxhealth.com
Autism is typically diagnosed though a 93-question questionnaire called the “Autism Diagnostic Interview, Revised” (ADI-R) test and/or via a behavior observation evaluation of the child in question with the “Autism Diagnostic Observation Schedule” (ADOS) exam.
The ADOS exam consists of 4 age-dependent modules that contain semi-structured activities designed to measure social interaction, communication, play and imaginative use of materials. Module 1 contains 10 activities and 29 items and is typically used for assessment of younger children.
Both the ADI-R and ADOS exams can take up to 3 hours or more and must be performed by a trained clinician with experience in diagnosing autism. One of the shortcomings aside from time is that the test results are analyzed subjectively and thereby prone to suffer from human error.
To remedy the backlog, time spent diagnosing and human error, researchers from Harvard Medical School have found a way using artificial intelligence to more accurately detect autism in children and establish a diagnosis in minutes rather than hours.
In a recent issue of Translational Psychiatry, researchers report their findings that by using computational algorithms that rely on a few questions and a short video of a child, a quick and accurate diagnosis is possible and could lead to earlier than average treatment. “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,” says Dennis Wall an associate professor of pathology and director of computational biology initiative at Harvard University’s Center for Biomedical Informatics.
The computational algorithms described in the published paper are referred to as “machine-learning algorithms”—a form of artificial intelligence where data is analyzed leading to a resulting diagnosis for autism that can be made efficiently, effectively and without the potential for subjective human error.
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