A team from the UPV has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. Credit: UPV. Public domain.
Scientists have developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence.
The system, developed by a team from the Human-Tech Institute-Universitat Politècnica de València has achieved an accuracy of over 85%, thus surpassing traditional methods of detecting autism in early childhood, which are usually based on psychological tests and interviews carried out manually.
In the study, the team from the Human-Tech Institute analysed the movements of children performing multiple tasks in virtual reality to determine which artificial intelligence technique is most appropriate for identifying ASD.
Mariano Alcañiz, director of the Human-Tech Institute at the UPV, said: “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.”
The virtual system consists of projecting, on the walls of a room or a large-format screen, a simulated environment in which the child’s image is integrated while performing multiple tasks, captured by a camera that analyses their movements.
Alcañiz added: “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.”
New artificial intelligence model
On the other hand, as explained by the researcher Alberto Altozano, who developed the AI model together with Professor Javier Marín, taking advantage of the experience acquired in the analysis of motor data, the UPV team compared traditional AI techniques with an innovative deep learning model.
Altozano said: “The results reveal that the proposed new model can identify ASD with greater precision and in a greater number of tasks within the VR experience.”
Once the child’s movements during the virtual experience have been automatically processed, the system establishes a diagnosis that, according to those responsible for the study, improves both the accuracy and the efficiency of conventional techniques.
Eight years of collaboration to improve early detection
Over the last eight years, the Human-Tech Institute of the UPV team has worked on perfecting the early detection of ASD, collaborating with the Red Cenit cognitive development centre, and developing and validating the semi-immersive system.
Within this framework, the researcher Eleonora Minissi recently presented her doctoral thesis, in which not only was the virtual reality system validated through studies with autistic children, but also the effectiveness of the various biomarkers measured during the virtual experience was compared.
Her research highlights that, despite the growing interest in social-visual attention in ASD, atypical motor patterns have received less diagnostic attention.
The researcher concluded that the ‘ease with which this data can be collected and its high effectiveness in detecting autism make the motor activity a promising biomarker’.
In addition, the latest results of the work of the Human-Tech Institute team suggest that the new AI can be adapted and trained to analyse the movements of ASD patients in other tasks.
Alcañiz said: “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?”
The study is published in the Expert Systems with Applications journal.
