New AI Model Shows Promise in Predicting Autism in Young Children
TEHRAN (Tasnim) – A recent study demonstrates a new machine learning model that can predict autism in young children with nearly 80% accuracy, offering potential for early diagnosis and intervention.
The newly developed machine learning model has demonstrated the ability to predict autism in young children from limited information, according to a study by Karolinska Institutet published in JAMA Network Open.
This model has the potential to facilitate the early detection of autism, which is critical for providing timely support.
"With an accuracy of almost 80% for children under the age of two, we hope that this will be a valuable tool for health care," said Kristiina Tammimies, Associate Professor at KIND, the Department of Women's and Children's Health at Karolinska Institutet, and the study's senior author.
The research team utilized data from the SPARK database, which includes information on approximately 30,000 individuals with and without autism spectrum disorders in the United States.
By analyzing a combination of 28 different parameters, the researchers developed four distinct machine-learning models to identify patterns in the data.
These parameters were selected based on information about children that can be obtained without extensive assessments and medical tests before 24 months of age.
The model that performed the best was named "AutMedAI."
Among approximately 12,000 individuals, the AutMedAI model accurately identified about 80% of children with autism.
Notably, age of first smile, first short sentence, and the presence of eating difficulties were strong predictors of autism when combined with other parameters.
"The results of the study are significant because they show that it is possible to identify individuals who are likely to have autism from relatively limited and readily available information," said Shyam Rajagopalan, the study's first author and an affiliated researcher in the same department at Karolinska Institutet.
He is also an assistant professor at the Institute of Bioinformatics and Applied Technology in India.
According to the researchers, early diagnosis is crucial for implementing effective interventions that can help children with autism develop optimally.
"This can drastically change the conditions for early diagnosis and interventions, and ultimately improve the quality of life for many individuals and their families," Rajagopalan added.
In the study, the AI model showed strong results in identifying children with significant difficulties in social communication, cognitive abilities, and general developmental delays.
The research team is now focused on further refining and validating the model in clinical settings.
Additionally, they are working on integrating genetic information into the model, which could lead to even more precise and accurate predictions.
"To ensure that the model is reliable enough to be implemented in clinical contexts, rigorous work and careful validation are required. I want to emphasize that our goal is for the model to become a valuable tool for health care, and it is not intended to replace a clinical assessment of autism," Tammimies noted.