Large-scale Validation of a Scalable and Portable Behavioral Digital Screening Tool for Autism at Home

Autism, characterized by challenges in socialization and communication, benefits from early detection for prompt and timely intervention. Traditional autism screening questionnaires often exhibit reduced accuracy in primary care settings and significantly underperform underprivileged populations. We present findings on the effectiveness of an autism screening digital application (app) that can be administered at primary care clinics and also by caregivers at home. A large-scale validation was conducted with 1052 toddlers aged 16-40 months. Among them, 223 were subsequently diagnosed with autism. The age-appropriate interactive app utilized strategically designed stimuli, presented on the screen of the iPhone or iPad, to evoke behaviors related to social attention, facial expressions, head movements, blinking rate, and motor responses, which can be detected with the device's sensors and automatically quantified through computer vision (CV) and machine learning. The algorithm, combining various digital biomarkers, demonstrated strong accuracy: Area under the receiver operating characteristic curve (AUC) = 0.93, sensitivity = 86.0%, specificity = 91.0%, and precision = 71%, for distinguishing autistic versus non-autistic toddlers, marking a strong foundation as a digital phenotyping tool in the autism research, notably without any costly equipment like eye tracking devices and at home administered by caregivers.