EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders.
Gianpaolo AlvariLuca CovielloCesare FurlanelloPublished in: Brain sciences (2021)
The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.
Keyphrases
- autism spectrum disorder
- artificial intelligence
- machine learning
- randomized controlled trial
- attention deficit hyperactivity disorder
- intellectual disability
- clinical practice
- mental health
- big data
- public health
- single cell
- molecular dynamics
- type diabetes
- high throughput
- mass spectrometry
- climate change
- data analysis
- high speed
- convolutional neural network
- human health