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Researchers are evaluating a smartphone-based platform called BlinkLab to assist in diagnosing Autism Spectrum Disorder ASD by measuring facial and behavioral reflexes. This retrospective multi-center case-control study aims to determine how accurately these smartphone-based neurobehavioral tests can identify ASD compared to formal clinical diagnoses using machine learning. The study includes children with ASD and neurotypical children without psychiatric diagnoses to compare sensorimotor responses. The neurobehavioral evaluations involve two consecutive 15-minute tests where children watch an audio-normalized movie while auditory stimuli are delivered through headphones. BlinkLab measures spontaneous and stimulus-evoked postural, head, facial, and vocal responses, including specific reflex tests like the acoustically evoked eyelid startle reflex ASR, prepulse inhibition PPI, and habituation HAB. Facial landmarks are tracked using computer vision algorithms to record the childrens responses during each trial. Participants are children aged 3 to 12 years who undergo these non-invasive assessments. Researchers measure various outcomes such as device sensitivity and specificity, screen avoidance, postural stability, head rotations, mouth opening, vocalizations, and acoustic startle responses. The study collects detailed behavioral and neurometric data to evaluate the diagnostic accuracy of the smartphone-based tool. The total participation involves completing the two testing sessions under standardized conditions, with no treatment or medication changes involved.