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NCT Number: NCT05874466

SenseToKnow Autism Screening Device Validation Study

This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the detection of autism spectrum disorder in children 16-36 months of age.

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Key information

Conditions

Autism Autism Spectrum Disorder Autistic Disorder Child Development Disorders, Pervasive Disease Mental Disorders Neurodevelopmental Disorders Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

16 month–36 month

Sex eligibility

All sexes

Study type

Observational

Primary location

Duke University

Durham, North Carolina, 27705, United States

Location status: Recruiting

Location contact

Geraldine Dawson, PhD

CONTACT

919-668-0070

About this study

This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the classification of autism spectrum disorder when administered by parents in a sample of patients 16-36 months of age. The trial design is a non-interventional cross-sectional study comparing the SenseToKnow device classification of autism spectrum disorder ("autism") versus non-autism with the patient's diagnostic status based on expert clinical diagnosis in a population of pediatric patients.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Duke Health pediatric patient at enrollment
  • 16-<37 months of age at enrollment
  • Parent/legal guardian speaks English or Spanish
  • Parent/legal guardian understands and voluntarily provides informed consent

Exclusion criteria

  • Severe motor impairment that precludes study measure completion
  • Known genetic disorders
  • Severe hearing or visual impairment as determined on physical examination according to parent report
  • Acute illnesses likely to prevent successful or valid data collection
  • Uncontrolled epilepsy or seizure disorder
  • History or presence of a clinically significant medical disease, or a mental state that could confound the study or be detrimental to the subject as determined by the investigator
  • Acute exacerbations of chronic illnesses likely to prevent successful or valid data collection
  • Receiving therapies that affect vision
  • Parent/legal guardian and/or investigator believes that the child will be unable/unwilling to sit in the parent's lap to watch the app videos
  • Parent/legal guardian indicates that they or their child is unwilling or unable to complete the app administration, surveys, or diagnostic assessment
  • Participants who are otherwise judged as unable to comply with the protocol by the investigator
  • Any other factor that the investigator feels would make the study measures invalid

Treatment and study plan

Primary outcomes

  1. Sensitivity of the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data and (2) expert clinical diagnosis / #participants positive for autism on both SenseToKnow and expert clinical diagnosis

  2. Specificity of the SenseToKnow screening device based on machine earning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data, and (2) expert clinical diagnosis / #participants negative for autism on autism by expert clinical diagnosis

Secondary outcomes

  1. Positive Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    The likelihood that a participant with a positive test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence.

  2. Negative Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    The likelihood that a participant with a negative test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence.

  3. Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow screening device (using the SenseToKnow digital data and SenseToKnow Caregiver survey data) for autism versus non-autism classification

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data, in comparison to expert clinical diagnosis.

  4. Sensitivity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / # participants positive for autism on expert clinical diagnosis

  5. Specificity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / #participants negative for autism on expert clinical diagnosis.

  6. Positive Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    The likelihood that a participant with a positive test result has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence.

  7. Negative Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    The likelihood that a participant with a negative test result does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence.

  8. Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow device using only the SenseToKnow digital data for autism versus non-autism classification

    Time frame: Will be calculated based on data from Baseline/Timepoint 1

    Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using only the SenseToKnow digital data, in comparison to expert clinical diagnosis.

Study contacts

Contact information is provided by the study sponsor or research team.

Charlotte Stoute, BA

CONTACT

[email protected]

919-681-9730

Geraldine Dawson, PhD

CONTACT

[email protected]

9196680070

Sponsors and collaborators

Lead sponsor

Duke University

Other

Collaborators

  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

Registry information

Official study title

SenseToKnow STAR Study: A Study of Technologies for Assessing Children's Development

Important dates

Study start
2023
Primary completion
2027
Study completion
2027
First posted
May 25, 2023
Registry last updated
Mar 6, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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