Autism, Autism Spectrum Disorder
Conditions
Keywords
Diagnosis, Digital device
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Duke University
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
16 Months to 36 Months
Healthy volunteers
Yes
Inclusion criteria
1. Duke Health pediatric patient at enrollment 2. 16-\<37 months of age at enrollment 3. Parent/legal guardian speaks English or Spanish 4. Parent/legal guardian understands and voluntarily provides informed consent
Exclusion criteria
1. Severe motor impairment that precludes study measure completion 2. Known genetic disorders 3. Severe hearing or visual impairment as determined on physical examination according to parent report 4. Acute illnesses likely to prevent successful or valid data collection 5. Uncontrolled epilepsy or seizure disorder 6. 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 7. Acute exacerbations of chronic illnesses likely to prevent successful or valid data collection 8. Receiving therapies that affect vision 9. 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 10. Parent/legal guardian indicates that they or their child is unwilling or unable to complete the app administration, surveys, or diagnostic assessment 11. Participants who are otherwise judged as unable to comply with the protocol by the investigator 12. Any other factor that the investigator feels would make the study measures invalid
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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 |
| 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 | 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
| Measure | Time frame | Description |
|---|---|---|
| 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 | 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. |
| 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 | 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. |
| 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 | 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. |
| Sensitivity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection | 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 |
| Specificity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection | 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. |
| 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 | 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. |
| 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 | 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. |
| 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 | 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. |
Countries
United States
Contacts
CONTACTGeraldine Dawson, PhD
CONTACTCharlotte Stoute, BA
PRINCIPAL_INVESTIGATORGeraldine Dawson, PhD
Duke University
Outcome results
None listed