SenseToKnow Autism Screening Device Validation Study
This study is testing a new tool called SenseToKnow to see how well it can identify autism spectrum disorder (ASD) in young children. The SenseToKnow device uses digital information combined with a survey filled out by parents or caregivers. We want to see how accurate this device is compared to a diagnosis made by an expert doctor. Children between 16 months and 36 months old who are patients at Duke Health can join. Your child cannot have severe motor problems or known genetic disorders to participate. The study is looking for 350 participants and is currently evaluating its status.
- Study design
- This is an observational study that compares the SenseToKnow device's results with an expert doctor's diagnosis. It is a double-blind study, meaning neither you nor the researchers will know the device's classification during the study.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- The main results will be calculated based on data collected at the beginning of the study (Baseline/Timepoint 1).
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
SenseToKnow Autism Screening Device Validation Study
At a glance
Conditions
NCT05874466
Where you'd take part
This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
Duke University
Durham, North Carolinastudy coordinator listed
Recruiting
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Study leadership
- Geraldine Dawson, PhD · PRINCIPAL_INVESTIGATOR · Duke University
Who to contact
Opens a ready-to-send draft in your own email app — review before sending.
What this trial measures
- 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 detectionWill 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 detectionWill 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