Cognoa Demonstrates Advances of its AI-Based Technology for Identifying Autism in Children

Two clinical studies published in peer-reviewed journals validate continued accuracy improvements of the company’s artificial intelligence (AI) platform

Cognoa, a digital behavioral health company, today announces results from a clinical study of its second-generation AI based algorithms which demonstrates accuracy improvements over its first-generation screening algorithm in supporting the diagnosis of autism spectrum disorder (ASD) in young children. Published in Journal of the American Medical Informatics Association, the data provides the foundation for continued clinical validation of Cognoa’s technology on its path toward becoming a commercially available diagnostic device to be used by physicians.

The multi-center clinical study found Cognoa’s improved second-generation machine learning algorithm was reliable, efficient within clinical workflows and achieved significantly higher accuracy than current standard practice screeners on the same age span. Completed at a sensitivity and specificity of 90 percent and 60 percent, the results demonstrate the potential of AI-based technology to contribute to and improve the process of ASD detection of young children.

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“Too many children are still not getting diagnosed early enough to get maximum benefit from therapy,” said John N. Constantino, MD, of Washington University and Cognoa Advisory Board Member. “Cognoa is demonstrating that AI represents an important area of innovation and opportunity. As the incidence of autism has continued to increase and yet the average age of diagnosis has not improved, technologies like Cognoa’s are needed to empower physicians to diagnose ASD sooner and provide every child with the opportunity to benefit from earlier treatment and improved lifetime outcomes.”

Recent studies published by the Centers for Disease Control and Prevention (CDC) indicate that the prevalence of autism has increased to one in 59. However, the median age of diagnosis has not improved in over 15 years. In 2000, the median age of earliest diagnosis was 4.4 to 4.6 years. Currently, on average, children are still being diagnosed at age four or older, beyond the primary window of brain development when interventions have the greatest impact. Cognoa’s algorithm has been clinically validated to screen for autism as early as 18 months of age.

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“Families should not have to endure a lengthy and difficult diagnostic process for any aspect of their children’s health,” said Tom Megerian, MD, PhD, Clinical Director of the CHOC Children’s Thompson Autism Center. “Long waiting times place an enormous amount of stress on families and can seriously diminish a child’s ability to reach their full developmental potential. Cognoa’s approach is promising for its potential to enable clinicians and families to take appropriate actions sooner by reducing diagnosis waiting times.”

Cognoa’s first-generation algorithm, developed in 2015 for screening purposes, showed better accuracy across a broader age range than other screening tools in a clinical study published in Autism Research. The platform’s continued improvements, demonstrated by its second-generation AI algorithm developed in 2016 and used in the study published in JAMIA, indicate that Cognoa is advancing its machine learning platform as the basis for a reliable diagnostic tool to improve the timeliness of autism diagnosis within the critical early childhood years.

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Cognoa’s platform uses machine learning technology to determine the most predictive data to identify risks for behavioral delays. Cognoa enables the collection and analysis of key data inputs that otherwise would be difficult or impossible for physicians to collect. The machine learning algorithm analyzes this data quickly and accurately to make determinations of a child’s current and future state of behavioral health.

“One of the strengths of machine learning is the ability to evaluate performance and adapt to ensure high accuracy and clinical value on an ongoing basis,” said Dr. Sharief Taraman, who is a clinical informaticist and pediatric neurologist in addition to being the Chief Medical Officer of Cognoa. “We are committed to further advancing our technology, conducting rigorous scientific studies to support the safety and effectiveness of our devices, and working with parents and their physicians with the ultimate goal of increasing access to behavioral health diagnostics for all children during the critical early childhood years.”