OMB Individually Reported
TB Portals Outlier Detection Lambda Function
Low riskExact public inventory row
Description
The quality of chest X-ray (CXR) images uploaded by TB Portals Program partners varied significantly, and as the scale of images increased, NIAID needed a way to identify outliers in the imaging dataset.
Detailed example
Input: Dicom file. Output: classification of Outlier or not Outlier via the model.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
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
Detect potential low-quality chest X Rays to flag as potentially being unsuitable for AI/ML training and flag for quality improvement.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Existing TB Portals images are used to train, fine tune and evaluate performance of the model.