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

Condition(s) studied

Investigational drug(s) / intervention(s)

BlinkLab Dx1: BlinkLab Dx1 is a machine learning-based algorithm that generates a continuous score reflecting likelihood of autism from neurobehavioral responses (including measures such as pre-pulse inhibition and habituation, motor activity, and vocalizations) recorded during standardized video-watching sessions in a mobile application. Pre-specified thresholds, determined during model development and fixed prior to evaluation in the independent test dataset, are applied to categorize results as " Positive for autism", "Intermediate" or "Nagative for autism". Results in the intermediate category are considered indeterminate and are excluded from primary diagnostic performance analyses, which are based on participants with definitive positive or negative test results.

Retrospective Neurodevelopmental Diagnostic Assessment: The clinical reference standard is a retrospective neurodevelopmental diagnostic assessment conducted within 12 months prior to enrollment, based on DSM-5 criteria. Participants are classified as having or not having an autism diagnosis based on the documented diagnostic conclusions of these assessments.

Study summary

This observational study aims to evaluate how patterns of behavioral and sensorimotor responses measured using the BlinkLab Dx1 smartphone application relate to autism diagnoses in children ages 2 to 11. BlinkLab Dx1 is a non-invasive, smartphone-based application under development as a diagnostic aid for healthcare providers assessing autism.

In this study, children who have undergone a neurodevelopmental assessment within the past 12 months will complete two short, video-based sessions using the BlinkLab Dx1 app. The app presents visual and auditory stimuli and records reflexive sensorimotor responses and patterns of repetitive behavior. Additionally, primary caregivers will answer a short questionnaire in the app about symptoms and development. Information about prior neurodevelopmental assessments, including documented DSM-5-based diagnoses from routine clinical practice, will be collected retrospectively.

The study will examine how the app's neurobehavioral measurements relate to previously assigned clinical diagnoses. These paired data will be used to develop and evaluate a machine learning-based algorithm using separate training and testing datasets to assess whether patterns measured by BlinkLab Dx1 can help distinguish children with autism from children without an autism diagnosis.

This study does not involve any treatment or medical intervention.

Eligibility

Primary outcome measure(s)

• Sensitivity of BlinkLab Dx1 — Baseline (including index test completed during the enrollment period and reference standard diagnosis from assessments conducted within 12 months prior to enrollment)
  Sensitivity of BlinkLab Dx1, defined as the proportion of participants with a positive clinical reference standard diagnosis of autism who are correctly identified as positive by the index test.
• Specificity of BlinkLab Dx1 — Baseline (including index test completed during the enrollment period and reference standard diagnosis from assessments conducted within 12 months prior to enrollment)
  Specificity of the BlinkLab Dx1 algorithm, defined as the proportion of participants without a clinical reference standard diagnosis of autism who are correctly identified as negative by the index test.

Trial sites (1)

Other trials for the same condition