TB Portals Outlier Detection Lambda Function

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

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2021-01 (month precision)Source: Jan-21

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

Detect potential low-quality chest X Rays to flag as potentially being unsuitable for AI/ML training and flag for quality improvement.

System outputs

Input: Dicom file. Output: classification of Outlier or not Outlier via the model.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

NIAID TB Portals

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Deloitte, Guidehouse, Research Data and Communication Technologies Corp.

Vendors (standardized)

DeloitteGuidehouse

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

Yes

Demographic features

k) None of the above

Training and evaluation data

Existing TB Portals images are used to train, fine tune and evaluate performance of the model.

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.

Potential impacts and how they were identified

Not reported