HHS · no agency ID · record R0839

Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations

Department of Health and Human Services · HHS/FDA/CDER

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact
Impact justification: 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.
Start date: 2025-01 (month precision)Source: Jan-25
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Health and Medical
Operational functions: Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

To advance novel endpoints in drug development, potentially leading to better ways to measure treatment effectiveness for skin conditions affecting children and adults.

System outputs

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.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): Emerald Innovations
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: The validation data is being collected as part of the study.
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported