eockfen/EyeTism
Early detection of Autism Spectrum Disorder (ASD) is crucial for children's development, yet the diagnostic procedure remains challenging. EyeTism employs machine learning on eye tracking data from both high-functioning ASD and typically developing children (TD) to create a diagnostic tool based on their distinct visual attention patterns.
This tool helps pediatricians and child development specialists identify Autism Spectrum Disorder (ASD) in children aged 8-15 years by analyzing their unique visual attention patterns. It takes eye-tracking data collected while a child views specific images and outputs insights for diagnostic evaluation. The primary users are medical professionals focused on child diagnosis and development.
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Use this if you are a pediatrician or child development specialist seeking an integrative tool to assist in the diagnosis of ASD based on a child's gaze behavior when viewing images.
Not ideal if you are looking for a standalone diagnostic tool, as this project provides an assistive tool based on specific eye-tracking data, not a complete diagnostic solution.
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Last pushed
May 02, 2024
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