Researchers at Karolinska Institutet have developed a machine learning model, AutMedAI, capable of predicting autism in children under two with nearly 80% accuracy, using a set of 28 parameters easily gathered before the age of 24 months.
The study, published in JAMA Network Open, highlights the model’s ability to identify key predictors like the age of first smile and presence of eating difficulties. This breakthrough promises to facilitate early interventions, enhancing the quality of life for affected individuals and their families.
Autism Prediction Model
“With an accuracy of almost 80 percent for children under the age of two, we hope that this will be a valuable tool for healthcare,” says Kristiina Tammimies, Associate Professor at KIND, the Department of Women’s and Children’s Health, Karolinska Institutet and last author of the study.
The research team used a large US database (SPARK) with information on approximately 30,000 individuals with and without autism spectrum disorders.
By analyzing a combination of 28 different parameters, the researchers developed four distinct machine-learning models to identify patterns in the data. The parameters selected were information about children that can be obtained without extensive assessments and medical tests before 24 months of age. The best-performing model was named ‘AutMedAI’.
Significance and Potential Impact
Among about 12,000 individuals, the AutMedAI model was able to identify about 80% of children with autism. In specific combinations with other parameters, age of first smile, first short sentence and the presence of eating difficulties were strong predictors of autism.
“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,” says study first author Shyam Rajagopalan, an affiliated researcher at the same department at Karolinska Institutet and currently assistant professor at the Institute of Bioinformatics and Applied Technology, India.
Enhancing Early Diagnosis and Intervention
Early diagnosis is critical, according to the researchers, to implement 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,” says Shyam Rajagopalan.
Future Directions and Model Validation
In the study, the AI model showed good results in identifying children with more extensive difficulties in social communication and cognitive ability and having more general developmental delays.
The research team is now planning further improvements and validation of the model in clinical settings. Work is also underway to include genetic information in the model, which may lead to even more specific and accurate predictions.
Conclusion and Clinical Implementation
“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,” says Kristiina Tammimies.
Publication: “Machine Learning Prediction of Autism Spectrum Disorder from a Minimal Set of Medical and Background Information” by Shyam Sundar Rajagopalan, Yali Zhang, Ashraf Yahia, Kristiina Tammimies, 19 August 2024, Jama Network Open.
DOI: 10.1001/jamanetworkopen.2024.29229
The study was funded by the Swedish Foundation for Strategic Research, Hjärnfonden and Stratneuro.