“Proof-Of-Concept” Evaluation of an Automated Sputum Smear Microscopy System for Tuberculosis Diagnosis

Background “TBDx” is an innovative smear microscopy system that automatically loads slides onto a microscope, focuses and digitally captures images and then classifies smears as positive or negative using computerised algorithms. Objectives To determine the diagnostic accuracy of TBDx, using culture as the gold standard, and compare this to a microscopist's diagnostic performance. Methods This study is nested within a cross-sectional study of tuberculosis suspects from South African gold mines. All tuberculosis suspects had one sputum sample collected, which was decontaminated prior to smear microscopy, liquid culture and organism identification. All slides were auramine-stained and then read by both a research microscopist and by TBDx using fluorescence microscopes, classifying slides based on the WHO classification standard of 100 fields of view (FoV) at 400× magnification. Results Of 981 specimens, 269 were culture positive for Mycobacterium tuberculosis (27.4%). TBDx had higher sensitivity than the microscopist (75.8% versus 52.8%, respectively), but markedly lower specificity (43.5% versus 98.6%, respectively). TBDx classified 520/981 smears (53.0%) as scanty positive. Hence, a proposed hybrid software/human approach that combined TBDx examination of all smears with microscopist re-examination of TBDx scanty smears was explored by replacing the “positive” result of slides with 1–9 AFB detected on TBDx with the microscopist's original reading. Compared to using the microscopist's original results for all 981 slides, this hybrid approach resulted in equivalent specificity, a slight reduction in sensitivity from 52.8% to 49.4% (difference of 3.3%; 95% confidence interval: 0.2%, 6.5%), and a reduction in the number of slides to be read by the microscopist by 47.0%. Discussion Compared to a research microscopist, the hybrid software/human approach had similar specificity and positive predictive value, but sensitivity requires further improvement. Automated microscopy has the potential to substantially reduce the number of slides read by microscopists.