Access to autism evaluation through specialty care is notorious for long wait times across the United States. In Missouri, many families wait nearly a year for a diagnostic appointment. AI could be a solution to reducing wait times, according to researchers at the University of Missouri School of Medicine.
Lead author Kristin Saul and her team partnered with Cognore to test the FDA-approved medical device CanvasDx for primary care clinicians in areas with no autism care. AI algorithms are incorporated into patient data to predict a positive or negative diagnosis of autism depending on the information provided. If a clear prediction cannot be made, an “uncertain” result is obtained.
“Our mission is to increase access to best practices in autism care throughout rural and underserved communities,” Saul said. “To consider CanvasDx as a potential tool for best practices, we tapped into the ECHO Autism community, which trains primary care clinicians in autism care across Missouri and beyond.”
Children in rural Missouri often wait longer to be evaluated for autism, giving families an opportunity to get the care they need. According to the study, a trip to a specialty care center meant traveling an average distance of 97 miles. By keeping care in the community, the family saved gas and was able to receive a diagnosis five to seven months earlier than if they had waited.
“Devices like CanvasDx, especially when used by clinicians with autism experience, can help speed diagnosis, so children can quickly access services that support them,” Sohl said. “It can also support clinicians and streamline the assessment process.”
In this study, using data from 80 children, the device produced definitive results in 52% of patients, but did not provide false-positive or false-negative diagnoses or contradict the clinician's diagnosis. Saul said this highlights the need for clinicians to be educated about autism assessment, diagnosis and care.
“Identifying autism and initiating individualized support for children with autism is critical to optimizing their outcomes,” Saul said. “Children with autism and their families deserve quality, timely access to local care and expertise. AI-integrated devices like CanvasDx can speed up the diagnostic process and add additional objective data to support primary care clinicians' diagnoses.”
Dr. Kristin Saul is a pediatrician at MU Health Care and professor of pediatrics at Mizzou School of Medicine. She is the Founder and Executive Director of the ECHO Autism Program and the Medical Director of the Missouri Telehealth Network (MTN) and the Office of Continuing Education for Health Professionals.
“Integrating an Artificial Intelligence-Based Autism Diagnostic Device into the ECHO Autism Primary Care Workflow: A Prospective Observational Study” was recently published in JMIR Formative Research. In addition to Saul, Mizzou study authors include ECHO autism lived experience expert Alicia Brewer Curran; Melissa Mahflin, Associate Director of Data and Evaluation for the ECHO Autism Community. and Valeria Nanclares, Director of Global Programs and Extension for the ECHO Autism Community. Eric Linstead, Kellyanne Heintz, Elia Eiroa Ledo, Carmela Salomon, Minda Seal, and Shareef Talaman contributed.
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