Autism: personal videos can help with diagnosis

American researchers have asked a group of families to film their children interacting with objects and people. They tried eight models of automated learning to diagnose autism, which helps to "streamline the process and make it much more effective," according to the study published in the scientific journal PLOS Medicine.

The study was developed by a team from the Faculty of Medicine at Stanford University and was led by Dennis Wall, professor of pediatrics and biomedical data science of this California city.

Each of the models contained "a set of algorithms that included 5 to 12 behavioral characteristics of children and this gave a general rating indicating whether the child was autistic, "he explained.

How videos were treated

Wall explained that to evaluate the models, they asked the families recruited for the survey to send us home videos of 1 to 5 minutes. in which the faces and hands of the children were shown and their "social interaction as well as the use of toys, pencils and utensils" were captured.. From these images, 116 boys aged 4 years and 10 months on average were diagnosed with autism and 46 others (with an average of 2 years and 11 months) developed it, he explained.

Nine expert reviewers have badyzed the videos with the help of a Questionnaire of 30 questions with "yes" or "no" responses, based on typical autism behaviors, which were then incorporated into the eight mathematical models.

The model that gave the best results is one that has identified 94.5% of children with autism and 77.4% of those with non autistic children. For an audit of the results, they evaluated 66 More Videoshalf of them children with autism. The same model correctly identified 87.8% of children with autism and 72.7% of those who did not have this disorder.

Another benefit of using personal videos for the the diagnosis do they "take the child into their natural environment", in contrast to the clinical evaluation that is carried out in a "medium that can be rigid and artificial and cause atypical behaviors". "We have shown that we can identify a small group of behavioral characteristics that are highly aligned with clinical outcomes and that non-experts can evaluate these characteristics quickly and independently in a virtual online environment in minutes," Wall said.