Delivering Safe, AI-Powered HIV Testing With 98% Accuracy

The project’s complexity stemmed from the unpredictable nature of Rapid Diagnostic Test (RDT) images. Many were poorly lit, blurry, or overexposed, making it hard to distinguish result lines. Others included personally identifiable information (PII), requiring secure redaction. Inconsistent cassette formats and brand variations added further difficulty in creating uniform data for model training. To complicate matters, a large volume of images lacked basic labeling. Cassette types had to be manually identified, and faint or unclear test lines demanded expert interpretation. These issues made automation alone insufficient, requiring a careful, human-led annotation approach to ensure precision, safety, and model reliability.

To meet these challenges, IndiVillage deployed a skilled team to annotate over 631,000 RDT images with both accuracy and speed. The team visually assessed each cassette, classified test outcomes, and extracted essential metadata, including RDT brand, patient info, and batch IDs. Ambiguous or unclear cases were reviewed twice, and escalated to medical professionals when needed. A rigorous feedback loop improved consistency over time, while low-quality or PII-containing images were flagged and filtered out. In total, more than 185,000 high-quality image annotations were processed with standardized tagging for downstream ML development. This expert-driven pipeline ensured clean, structured data, laying a strong foundation for AI models that could function reliably in real-world diagnostics.

Audere’s AI models, fueled by IndiVillage’s human-in-the-loop data pipeline, achieved up to 98% accuracy for certain RDT types in real-world scenarios. The clean, well-tagged data enabled rapid AI deployment in diverse geographies, accelerating diagnosis, containment, and care, especially in underserved regions with limited clinical infrastructure. This partnership helped Audere bring scalable, self-test interpretation to the global stage, bridging the gap between frontline healthcare needs and advanced AI capabilities. With IndiVillage’s support, AI was no longer just a lab tool, it became a trusted decision-making ally for patients and health workers worldwide, delivering impact where it matters most.