Accuracy of computer-aided chest X-ray in community-based tuberculosis screening: Lessons from the 2016 Kenya National Tuberculosis Prevalence Survey

• Published in: PLOS global public health 2022-01, Vol.2 (11), p.e0001272
• Main Authors: Brenda Mungai, Jane Ong'angò, Chu Chang Ku, Marc Y R Henrion, Ben Morton, Elizabeth Joekes, Elizabeth Onyango, Richard Kiplimo, Dickson Kirathe, Enos Masini, Joseph Sitienei, Veronica Manduku, Beatrice Mugi, Stephen Bertel Squire, Peter MacPherson, IMPALA Consortium
• Format: Article
• Language: English
• ISSN: 2767-3375
• DOI: 10.1371/journal.pgph.0001272

Community-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening. CXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs. Of 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58-82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44-57, p