Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations
Description
Intended to solve the problem of lacking objective, quantitative methods to assess nocturnal scratching in children with atopic dermatitis. It aims to create a digital endpoint that can accurately measure scratching behavior to evaluate the efficacy and performance of FDA-regulated treatments for atopic dermatitis and pruritus, addressing an unmet need in clinical assessment.
Detailed example
A digital endpoint that can accurately measure scratching behavior to evaluate the efficacy and performance of FDA-regulated treatments for atopic dermatitis and pruritus, addressing an unmet need in clinical assessment.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
To advance novel endpoints in drug development, potentially leading to better ways to measure treatment effectiveness for skin conditions affecting children and adults.
Audit / financial statement impact
The purpose of this study is to validate the Emerald technology to see if it accurately detects the motion of an individual scratching. The act of scratching would not be considered to have a significant effect on human health and safety, which means this study is not a high-impact AI use case based on the definition in OMB M-25-21.
Controls / human review
ATO: No; PIA: Not published
Data needed
The validation data is being collected as part of the study.