Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder

• Main Authors: Huynh, Marie, Kline, Aaron, Surabhi, Saimourya, Dunlap, Kaitlyn, Mutlu, Onur Cezmi, Honarmand, Mohammadmahdi, Azizian, Parnian, Washington, Peter, Wall, Dennis P
• Format: Journal Article
• Language: English
• Published: 23-08-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2408.13255

Early detection of autism, a neurodevelopmental disorder marked by social
communication challenges, is crucial for timely intervention. Recent
advancements have utilized naturalistic home videos captured via the mobile
application GuessWhat. Through interactive games played between children and
their guardians, GuessWhat has amassed over 3,000 structured videos from 382
children, both diagnosed with and without Autism Spectrum Disorder (ASD). This
collection provides a robust dataset for training computer vision models to
detect ASD-related phenotypic markers, including variations in emotional
expression, eye contact, and head movements. We have developed a protocol to
curate high-quality videos from this dataset, forming a comprehensive training
set. Utilizing this set, we trained individual LSTM-based models using eye
gaze, head positions, and facial landmarks as input features, achieving test
AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we
applied late fusion techniques to create ensemble models, improving the overall
AUC to 90%. This approach also yielded more equitable results across different
genders and age groups. Our methodology offers a significant step forward in
the early detection of ASD by potentially reducing the reliance on subjective
assessments and making early identification more accessibly and equitable.