2_use_case_name: Automated interpretation of Line Probe Assay strips using computer vision
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/India
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The AI automates the interpretation of Line Probe Assay (LPA) results, enhancing accuracy, efficiency, and consistency in detecting drug-resistant TB. It reduces manual errors, accelerates diagnosis, and integrates with the Ni-kshay platform to support timely treatment decisions for TB patients.
12_outputs: The AI system detects the bands present on the LPA strip and outputs the sensitivity to different drugs, automatically classifying the test results as resistant or susceptible. It generates a final diagnostic report that helps clinicians make informed treatment decisions based on the presence or absence of drug resistance
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 6/1/2020
19_date_acq_dev_began: 7/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids:  72038620CA00006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: - Misinterpretation of Results: Incorrect interpretation of AI outputs could lead to delayed or incorrect treatment. This was identified through validation studies and expert reviews.
- Integration Disruption: The AI model is hosted in Ni-kshay (Official Health Management Information System for TB in India). Any outage or disruption in Ni-kshay could impact the AI's availability and functionality. This risk was identified during system integration and stress testing.
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
56_monitor_postdeploy: Established Process of Machine Learning Operations: Alongside automated testing and drift detection, model re-training and re-deployments are supported by continuous integration pipelines that are managed by machine learning and data engineers on the platform, adapting work done by data science team into repeatable scripts for re-training and re-testing a model once deployed.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No