aceValidation of an AI-Driven Sputum Analysis Platform for Accurate and Reliable Tuberculosis Detection in South-Western Uganda
Ziehl-Neelsen (ZN) sputum smear microscopy remains an accessible method for detecting acid-fast bacilli (AFB), but its interpretation is dependent on specimen quality and trained microscopy personnel. Artificial intelligence (AI) may provide a microscopy reading aid that supports consistent identification of AFB-positive and AFB-negative smear images. To evaluate transfer-learning deep learning models for classification of AFB-positive and AFB-negative digitized ZN-stained sputum smear images generated in a Ugan…
A Swin Transformer-Tiny model separated AFB-positive from AFB-negative Ziehl-Neelsen smear images with 97.40% accuracy on an internal test set, suggesting a possible microscopy reading aid.
The small, balanced, single-centre internal test set and reliance on routine microscopy labels rather than an independent microbiological reference standard limit clinical interpretation.
Evidence
- Peer-reviewedMedical Devices: Evidence and Research2026-10-15
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Truvace Impact Record TRV-2026-2125, v1: “Validation of an AI-Driven Sputum Analysis Platform for Accurate and Reliable Tuberculosis Detection in South-Western Uganda.” Truvace, 2026-10-21. /record/TRV-2026-2125 (accessed at citation time). sha256 55ae425adbeb91ee…
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