A public service announcement from Autism Speaks offers information about the signs of autism. (Autism Speaks Publicity Board/TNS)
A machine learning model can identify most children with autism before age 2, research suggests, potentially speeding up the process of getting a diagnosis.
In a study that analyzed data from about 12,000 people in the U.S. with and without autism, researchers said their artificial intelligence model called AutMedAI could predict autism in children under age 2 with nearly 80% accuracy.
The model was based on 28 different parameters, all of which could be obtained without extensive evaluations or testing, according to Recommendations published this month in the journal JAMA Network Open.
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Some of the factors taken into account include the age at which the child first smiled, when he or she said a short sentence, and the presence of feeding difficulties.
“The results of the study are significant because they show that it is possible to identify individuals likely to have autism from relatively limited and readily available information,” said Shyam Rajagopalan, who led the study while working at the Karolinska Institute in Sweden.
According to the Centers for Disease Control and Prevention, while autism can be diagnosed with certainty as early as age 2, many children are much older before that happens. Early diagnosis is considered critical because it allows children to have access to intervention.
“This could dramatically change the conditions for early diagnosis and interventions and ultimately improve the quality of life for many individuals and their families,” Rajagopalan said of the study’s findings.
The researchers are now planning further improvements, including adding genetic information to the model, and want to study the approach in clinical settings.
“To ensure that the model is reliable enough to be applied in clinical settings, rigorous work and careful validation are required,” said Kristiina Tammimies, an author of the study from Karolinska Institutet. “I want to emphasize that our goal is for the model to become a valuable tool for medical care and it is not intended to replace a clinical assessment of autism.”
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