In the study, the research team of computer scientists and clinical psychologists specifically looked at differentiating between ASD and ADHD in school-aged children. graduates autism researchers Shuting Zheng, Emma Salzman and Somer Bishop from the University of California San Francisco and Catherine Lord from the University of California Los Angeles. In addition to Narayanan and Ardulov, co-authors of the study published in Scientific Reports are Victor Martinez and Krishna Somandepalli, both recent USC Ph.D. “By training the algorithm in this way, you’re optimizing it to be as effective as possible with the information collected so far.” “We wanted to maximize the diagnostic power of the interview by bootstrapping the clinician with an algorithm that can be more curious if it needs to be, but will also try to not ask more questions than it needs to,” said study lead author Victor Ardulov, a computer science Ph.D. The AI-based method takes the form of a computer adaptive test, powered by machine learning, that helps clinical practitioners decide what questions to ask next in real-time based on the caregivers’ previous responses. “As such, this system is difficult to administer and can produce false positives, or confound ASD as other comorbid conditions, such as attention deficit hyperactivity disorder (ADHD).”Īs a result, many children fail to get the treatments they need at a critical time.Īn interdisciplinary team led by USC computer science researchers, in collaboration with clinical experts and researchers in autism, hopes to improve this by creating a faster, more reliable and more accessible system to screen children for ASD. “In trying to discern and stratify a complex condition such as autism spectrum disorder, knowing what questions to ask and in what order becomes challenging,” said USC University Professor Shrikanth Narayanan, Niki and Max Nikias Chair in Engineering and professor of electrical and computer engineering, computer science, linguistics, psychology, pediatrics and otolaryngology. But these questionnaires are extensive, complicated and not foolproof. There is no lab test and no single identified genetic cause-instead, clinicians look at the child’s behavior and conduct structured interviews with the child’s caregivers based on questionnaires.

But despite being one of the most common developmental disabilities, impacting 1 in 54 children in the U.S., it’s not that easy to diagnose. Summary: A new machine-learning algorithm could help practitioners identify autism in children more effectively.įor children with autism spectrum disorder (ASD), receiving an early diagnosis can make a huge difference in improving behavior, skills and language development.

Usc cs mobile development professors