Development of an interactive tool of early social responsiveness to track autism risk in infants and toddlers.

AIM: To evaluate the psychometric properties of a 4-minute assessment designed to identify early autism spectrum disorder (ASD) status through evaluation of early social responsiveness (ESR). METHOD: This retrospective, preliminary study included children between 13 and 24 months (78 males, 79 females mean age 19.4mo, SD 3.1) from two independent data sets (an experimental/training sample [n=120] and a validation/test sample [n=37]). The ESR assessment examined social behaviors (e.g. eye contact, smiling, ease-of-social-engagement) across five common play activities (e.g. rolling a ball, looking at a book). Data analyses examined reliability and accuracy of the assessment in identifying ESR abilities and in discriminating children with and without ASD. RESULTS: Results indicated adequate internal consistency and test-retest reliability of the ESR assessment. Receiver operator curve analysis identified a cutoff score that discriminated infants with ASD-risk from peers in the training sample. This score yielded moderate sensitivity and high specificity for best-estimate ASD diagnosis in the validation sample. INTERPRETATION: Preliminary findings indicated that brief, systematic observation of ESR may assist in discriminating infants with and without ASD, providing concrete evidence to validate or supplement parents', pediatricians', or clinicians' concerns. Future studies could examine the utility of ESR 'growth curves'.

Detection of eye contact with deep neural networks is as accurate as human experts.

Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looking direction is a challenging task, but eye contact can be effectively captured by a wearable point-of-view camera which provides a unique viewpoint. While moments of eye contact from this viewpoint can be hand-coded, such a process tends to be laborious and subjective. In this work, we develop a deep neural network model to automatically detect eye contact in egocentric video. It is the first to achieve accuracy equivalent to that of human experts. We train a deep convolutional network using a dataset of 4,339,879 annotated images, consisting of 103 subjects with diverse demographic backgrounds. 57 subjects have a diagnosis of Autism Spectrum Disorder. The network achieves overall precision of 0.936 and recall of 0.943 on 18 validation subjects, and its performance is on par with 10 trained human coders with a mean precision 0.918 and recall 0.946. Our method will be instrumental in gaze behavior analysis by serving as a scalable, objective, and accessible tool for clinicians and researchers.