Approximately 75 million people in the world have been diagnosed with autism, a complex neurodevelopmental disorder that impacts individuals across a large scale of severity and symptoms.
Symptoms are scored from 15 to 60, with scores under 30 considered low, 30-36.5 at moderate level and 37 to 60 indicating severe autism.
Experts say that early intervention is imperative to help each individual meet their potential, no matter where they fall on the spectrum.
Researchers from York University in Toronto and University of Haifa have used machine learning to impart early autism diagnoses to make sure intervention is timely.
They used kinematic features, namely a natural grasping task with only two finger-tracking markers that are indicative of motor control integrity. Using reach-to-grasp movements as data with those on the spectrum and those not, they were able to use machine learning to determine autism identification at 95 percent accuracy.
These findings complement emerging views that movement variability may reveal autism subtypes and could enhance early detection or intervention strategies.
The study was published in Autism Research.
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