AI Model Shows 80% Accuracy in Early Autism Screening for Toddlers

Key Insights:

• AI model offers a new tool for early autism screening in toddlers, but experts warn against over-reliance on early psychiatric labels.
• AI can help identify autism risk in children under two with 80% accuracy, focusing on key developmental milestones like smiling and potty training.
• While promising, the AI model’s lower accuracy sometimes highlights the need for further refinement before clinical adoption.

Recent research has demonstrated the potential of artificial intelligence (AI) to assist in identifying toddlers at risk of autism, with an accuracy rate of about 80% for children under two.

The study, led by researchers from the Karolinska Institutet in Sweden, involved developing a machine learning-based screening system. Although the AI model cannot replace traditional diagnostic methods, it could aid in the early identification of children who may benefit from further clinical evaluation.

Dr. Kristiina Tammimies, a study co-author, emphasized the importance of early identification. “Using [the] AI model, it can be possible to use available information and identify individuals with an elevated likelihood for autism so that they can get earlier diagnosis and help,” Tammimies said. 

However, she cautioned that the model should not be viewed as a standalone diagnostic tool, reiterating that the final diagnosis should be conducted through standard clinical methods.

Development and Testing of the AI Model

The AI model was developed using data from the Spark study, a U.S. research initiative that provided information on 15,330 children diagnosed with autism and an equal number of children without the condition. The researchers selected 28 measures, easily obtainable before children reach 24 months, based on parent-reported information from medical and background questionnaires. These measures included age at first smile, eating behaviors, and age at first construction of longer sentences.

To create the model, the research team used machine learning to analyze patterns in the data, comparing the identified patterns between autistic and non-autistic children. After building and tuning four different models, the researchers selected the most effective one for further testing.

When applied to a separate dataset of 11,936 participants, the model correctly identified 78.9% of the children as either autistic or non-autistic. Specifically, the accuracy was 78.5% for children aged up to two years, 84.2% for those aged two to four years, and 79.2% for those aged four to ten years. 

An additional test using a dataset of 2,854 autistic individuals resulted in a lower accuracy rate of 68%, which the researchers attributed to differences in the dataset, including some missing parameters.

Factors Influencing AI Predictions

The study identified several significant key measures in predicting autism using the AI model. These included problems with eating certain foods, the age at which a child first constructed longer sentences, the age at which a child achieved potty training, and the age at which a child first smiled. The research team noted that these factors played a crucial role in the model’s ability to differentiate between autistic and non-autistic children.

Further analysis revealed that the model tended to identify autism more accurately in individuals who exhibited more severe symptoms and broader developmental issues. This finding suggests that the model might be more effective at recognizing cases with more noticeable developmental challenges accompanying autism.

Expert Caution on Early Diagnosis

Despite the promising results, some experts have raised concerns about the model’s ability to identify non-autistic children correctly. The model’s 80% accuracy rate means that 20% of non-autistic children could be incorrectly flagged as possibly autistic, leading to potential overdiagnosis and unnecessary stress for families.

Professor Ginny Russell from the University of Exeter expressed caution regarding the push for early diagnosis, particularly in very young children. Russell noted that it can be difficult to distinguish between toddlers with severe impairments and those who are simply developing slower but will eventually “catch up.” She recommended against applying psychiatric labels to children under the age of two based on limited behavioral indicators, such as eating patterns.

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