Video-Based Autism Detection with Deep Learning
This work addresses early ASD diagnosis for medical professionals using affordable video-based methods, but it is incremental as it applies existing deep learning techniques to a new data modality.
The paper tackles the problem of early detection of Autism Spectrum Disorder (ASD) in children by developing a deep learning model that analyzes video clips of reactions to sensory stimuli, achieving successful classification performance despite limited data and motion artifacts.
Individuals with Autism Spectrum Disorder (ASD) often experience challenges in health, communication, and sensory processing; therefore, early diagnosis is necessary for proper treatment and care. In this work, we consider the problem of detecting or classifying ASD children to aid medical professionals in early diagnosis. We develop a deep learning model that analyzes video clips of children reacting to sensory stimuli, with the intent of capturing key differences in reactions and behavior between ASD and non-ASD participants. Unlike many recent studies in ASD classification with MRI data, which require expensive specialized equipment, our method utilizes a powerful but relatively affordable GPU, a standard computer setup, and a video camera for inference. Results show that our model effectively generalizes and understands key differences in the distinct movements of the children. It is noteworthy that our model exhibits successful classification performance despite the limited amount of data for a deep learning problem and limited temporal information available for learning, even with the motion artifacts.