Researchers from the Human-Tech Institute-Universitat Politècnica de València in Spain developed a new way to detect Autism Spectrum Disorder (ASD) in children by using virtual reality and artificial intelligence, according to a study published in the Expert Systems with Applications journal.
According to the authors, the system can accurately detect 85% of cases, which is better than traditional methods of detecting autism in early childhood based on psychological tests and interviews carried out manually.
The new method involves analysing the movements of children performing multiple tasks in virtual reality to determine which artificial intelligence technique is most appropriate for identifying ASD.
“The use of virtual reality allows us to use recognisable environments that generate realistic and authentic responses, imitating how children interact in their daily lives. This is a significant improvement over laboratory tests, in which responses are often artificial. With virtual reality, we can study more genuine reactions and better understand the symptoms of autism,” said Mariano Alcañiz, director of the Human-Tech Institute at the UPV.
“This method standardises the detection of autism by analysing biomarkers related to behaviour, motor activity and gaze direction. Our system only requires a large screen and a type of camera that is already on the market and is cheaper than the usual test-based evaluation method. Without doubt, it would facilitate access to diagnosis as it could be included in any early intervention space.”
“The proposed new model can identify ASD with greater precision and in a greater number of tasks within the VR experience,” says Alberto Altozano, who developed the AI model together with Professor Javier Marín. After the assessment, the child’s movements are automatically processed, and the system reveals a diagnosis. According to the authors, this is more accurate and more efficient than conventional techniques.
Given these promising results, the authors suggest that the new AI can be adapted and trained to analyse the movements of ASD patients in other tasks. “This opens the door to future explorations of the motor symptomatology of autism such as: what are the motor characteristics of autistic children when walking or talking?“ concluded Alcañiz.
Altozano A, Minissi M, Alcañiz M, Marín-Morales J (2025) Introducing 3DCNN ResNets for ASD full-body kinematic assessment: A comparison with hand-crafted features. Expert Systems with Applications, 270: 126295 https://doi.org/10.1016/j.eswa.2024.126295