Evaluating BlinkLab Dx1 Smartphone App for Autism Diagnosis in Children Ages 2 to 11 Using Video-Based Behavioral and Sensorimotor Assessments Compared to Recent Clinical Diagnoses

What this Trial Is About

Researchers are evaluating how behavioral and sensorimotor responses measured by the BlinkLab Dx1 smartphone app relate to autism diagnoses in children aged 2 to 11. This observational study focuses on children who have undergone neurodevelopmental assessments within the past 12 months, using data to develop and assess a machine learning algorithm for autism diagnosis. The goal is to understand if patterns recorded by the app can help distinguish children with autism from those without, based on prior clinical diagnoses. Participants will complete two brief video-based sessions at home using the BlinkLab Dx1 app, which presents visual and auditory stimuli and records reflexive responses and repetitive behaviors. Caregivers will also complete a questionnaire about symptoms and development. The study involves no treatment or medical intervention and uses previously collected clinical data as a reference standard. The dataset is split into training and testing groups to develop and evaluate the algorithms accuracy in classifying autism status. During the study, researchers will collect app data and paired clinical diagnoses to assess diagnostic performance, including sensitivity and specificity. Additional measures include adverse events, usability, and autism symptom severity using the SRS-2 assessment. Participation involves remote sessions and questionnaires, with data collected prospectively and retrospectively. The study lasts for up to 30 days per participant and monitors for safety and usability during this period.

Online Evaluation of the Diagnostic Accuracy of BlinkLab's Digital Assessments for Autism