Scarpa’s team used machine learning technology to create an automated, self-scoring version of the Modified Checklist for Autism in Toddlers (M-CHAT)-Revised. The test is a 20-item parent survey administered during an 18- or 24-month well-child visit; it takes about 10 minutes to fill out and 5 minutes for pediatricians to score. Based on the results, pediatricians can follow up with children showing signs of autism and determine whether to send them for a full diagnostic evaluation.
The automated version takes scoring out of the pediatricians’ hands, sidestepping the perils of human judgment and providing a clear-cut verdict on whether a child needs further testing, Scarpa says.
Her team looked at survey responses for nearly 15,000 toddlers. About 50 percent of the toddlers are white, 20 percent are black and 30 percent are of mixed or other backgrounds; the children’s mothers have a college education on average. The study included a roughly equal number of boys and girls.
The researchers fed the survey results into an algorithm that scanned them for meaningful patterns. The algorithm could accurately detect autism using only 12 of the 20 survey items, the researchers found. The responses from the remaining eight items — including a question that asked if a toddler engages in pretend play — did not provide meaningful data, Scarpa says.
The algorithm could also indicate whether a boy was at low, medium or high risk of autism, based on the responses.
However, in girls, it missed the crucial middle ground: It picked up on girls at high risk of autism, and those at low risk, but was unable to identify girls with mild autism symptoms.
Most of the 12 key questions evaluate a child’s ability to share another person’s focus on an activity or object, known as joint attention.
Girls with severe autism tend to have trouble with joint attention, and the algorithm correctly sorted them into the high-risk group. But it missed girls with mild or moderate autism who can follow another person’s gaze and interpret social cues, lumping them into the low-risk group along with typically developing girls.
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