Researchers created an artificial intelligence-based tool to help primary care clinicians and pediatricians spot potential cases of the neurological condition, according to Brandon Keehn, PhD, associate professor in the Department of Speech, Language, and Hearing Sciences at Purdue University in West Lafayette, Indiana, and an author of the study.
"This is the first step in demonstrating both that eye-tracking biomarkers are sensitive to autism and whether or not these biomarkers provide extra clinical information for primary care physicians to more accurately diagnose autism," Keehn toldThe study took place between 2019 and 2022 and included 146 children between 14 and 48 months old who were treated at seven primary care practices in Indiana.
Keehn said his team is still a few steps away from determining how the model would work in a real clinical setting and that they are planning more research with a larger study population.
Source: Tech Daily Report (techdailyreport.net)
Autism Autism Spectrum Condition Autism Spectrum Disorder (ASD) Diagnosis-Related Groups Biomarker Biological Marker Multi-Biomarker Disease Activity MBDA Multibiomarker Disease Activity Eye Primary Care Children Child Childhood Pediatrics Kids Artificial Intelligence Deep Learning AI NPL Machine Learning ML Natural Language Processing
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