Signs and symptoms of autism in children are hard to detect but new research suggests that simple quantifiable measures of artificial intelligence could enable much earlier diagnosis of Rett syndrome and possibly other disorders with autism-like features.
The study published in the journal ‘Proceedings of the National Academy of Sciences’ unveiled a machine-learning algorithm that can spot abnormalities in pupil dilation that are predictive of autism spectrum disorder (ASD) in mouse models.
It further showed that the algorithm can accurately detect if a girl has Rett syndrome, a genetic disorder that impairs cognitive, sensory, motor, and autonomic function starting at 6 to 18 months of age, as well as autism-like behaviours.
Researchers hope this system could provide an early warning signal not just for Rett syndrome but for ASD in general. In the future, they believe it could also be used to monitor patients’ responses to treatments; currently, a clinical trial is testing the drug ketamine for Rett syndrome, and a gene therapy trial is planned.
Researchers began with the idea that people on the autism spectrum have altered behavioural states. Prior evidence indicates that the brain’s cholinergic circuits, which are involved in arousal, are especially perturbed, and that altered arousal affects both spontaneous pupil dilation/constriction and heart rate.
Also, spontaneous pupil dilation and constriction were altered even before the animals began showing ASD-like symptoms, the team found.
In a previous study with researchers showed that visual evoked potentials, an EEG measure of visual processing in the brain, could also serve as a potential biomarker for Rett syndrome.
Researchers believe that together, such biomarkers could offer robust yet affordable screening tools for infants and toddlers, warning of impending neurodevelopmental problems and helping to follow the progression of their development or treatment. (ANI)
