P-1390. Accuracy of computer-aided detection of chest x-rays for pulmonary tuberculosis among adults with Xpert Ultra trace-positive sputum

Abstract Background The clinical significance of low-level positive results from molecular testing of sputum for tuberculosis (TB) remains uncertain, and additional diagnostic testing, such as chest X-ray, might help to guide patient care. In high-TB-burden settings, where radiologists are often unavailable, artificial intelligence-based computer-aided detection (CAD) may be used to analyze chest X-rays, but its accuracy in this context remains unclear.Table 1.TB status determination at three months among outpatients with Xpert Ultra trace-positive sputum – overall and by X-ray score category and/or clinical risk subgroup, using two definitions of TB based on treatment decision with or without bacteriological confirmation.Figure 1.Receiver operating characteristic curves showing the performance of computer-aided detection software (qXR v4) on a baseline X-ray, for classifying bacteriologically confirmed and/or unconfirmed tuberculosis among individuals with an initial trace-positive sputum Xpert Ultra diagnostic result and up to three months of further diagnostic evaluation. Methods We evaluated CAD accuracy among adults who presented to clinics in Uganda and South Africa and tested trace-positive on sputum Xpert Ultra (“Xpert”). Participants underwent repeat sputum Xpert, two mycobacterial cultures, chest X-ray, and HIV testing, and those not started on treatment after this baseline evaluation repeated sputum Xpert and culture testing at 1 and 3 months to further clarify TB status. Chest X-ray images were retrospectively analyzed by CAD software (qXR v4, Qure.ai). We evaluated CAD accuracy compared to two TB definitions: a composite definition that included clinical diagnoses, and a second requiring bacteriological confirmation.Figure 2.Distribution of X-ray scores from computer-aided detection software (qXR v4), among individuals with trace-positive sputum at clinics in Uganda and South Africa, colored by TB diagnostic outcomes and stratified by history of previous tuberculosis treatment and by HIV status. Results Among 276 participants, median age was 37 years (interquartile range 30-46), 141 (51%) were female, 156 (57%) were HIV-positive, and 102 (37%) had prior treatment for TB. The median X-ray score was 0.63 (interquartile range 0.14-0.91), and 153 (55%) had a score above 0.5 (the manufacturer-recommended threshold). TB was diagnosed in 131 patients (47%), including 76 with bacteriological confirmation. CAD had an area under the curve (AUC) of 0.65 (95% confidence interval 0.58–0.71) using a composite definition and 0.65 (0.57–0.73) for bacteriologically confirmed TB. An X-ray score threshold of 0.5 had sensitivity and specificity of 66% and 54%, respectively, for bacteriologically confirmed TB. Lowering the threshold to 0.2 increased sensitivity to 80% but decreased specificity to 37%. The AUC of CAD was higher among patients without prior TB treatment and among those without HIV infection (Figure 1). Conclusion Chest x-ray with CAD could serve as a supplementary tool for diagnosing TB in adults with Xpert trace-positive sputum, particularly those without prior tuberculosis or HIV infection. However, a low X-ray score did not reliably rule out TB disease. Disclosures Paul K. Drain, MD, MPH, Abbott Diagnostics: Advisor/Consultant|Abbott Diagnostics: Grant/Research Support|Giner: Advisor/Consultant|OraSure: Advisor/Consultant|Revvity: Advisor/Consultant|Revvity: Grant/Research Support|Roche: Advisor/Consultant|Roche: Grant/Research Support

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Journal
Open Forum Infectious Diseases
Published
2026-01-01
DOI
https://doi.org/10.1093/ofid/ofaf695.1577
Primary Topic
COVID-19 diagnosis using AI
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article
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article

P-1390. Accuracy of computer-aided detection of chest x-rays for pulmonary tuberculosis among adults with Xpert Ultra trace-positive sputum

Mariam Nantale, Achilles Katamba, Annet Nalutaaya, Emily A. Kendall et al.
Open Forum Infectious Diseases
COVID-19 diagnosis using AI
article

P-1390. Accuracy of computer-aided detection of chest x-rays for pulmonary tuberculosis among adults with Xpert Ultra trace-positive sputum

Mariam Nantale, Achilles Katamba, Annet Nalutaaya, Emily A. Kendall, Patrick Biché, Joowhan Sung, James Mukiibi, Caitlin Visek, Paul Drain, Douglas Wilson, Gabrielle Stein, Ronit Dalmat
article en

Abstract

Abstract Background The clinical significance of low-level positive results from molecular testing of sputum for tuberculosis (TB) remains uncertain, and additional diagnostic testing, such as chest X-ray, might help to guide patient care. In high-TB-burden settings, where radiologists are often unavailable, artificial intelligence-based computer-aided detection (CAD) may be used to analyze chest X-rays, but its accuracy in this context remains unclear.Table 1.TB status determination at three months among outpatients with Xpert Ultra trace-positive sputum – overall and by X-ray score category and/or clinical risk subgroup, using two definitions of TB based on treatment decision with or without bacteriological confirmation.Figure 1.Receiver operating characteristic curves showing the performance of computer-aided detection software (qXR v4) on a baseline X-ray, for classifying bacteriologically confirmed and/or unconfirmed tuberculosis among individuals with an initial trace-positive sputum Xpert Ultra diagnostic result and up to three months of further diagnostic evaluation. Methods We evaluated CAD accuracy among adults who presented to clinics in Uganda and South Africa and tested trace-positive on sputum Xpert Ultra (“Xpert”). Participants underwent repeat sputum Xpert, two mycobacterial cultures, chest X-ray, and HIV testing, and those not started on treatment after this baseline evaluation repeated sputum Xpert and culture testing at 1 and 3 months to further clarify TB status. Chest X-ray images were retrospectively analyzed by CAD software (qXR v4, Qure.ai). We evaluated CAD accuracy compared to two TB definitions: a composite definition that included clinical diagnoses, and a second requiring bacteriological confirmation.Figure 2.Distribution of X-ray scores from computer-aided detection software (qXR v4), among individuals with trace-positive sputum at clinics in Uganda and South Africa, colored by TB diagnostic outcomes and stratified by history of previous tuberculosis treatment and by HIV status. Results Among 276 participants, median age was 37 years (interquartile range 30-46), 141 (51%) were female, 156 (57%) were HIV-positive, and 102 (37%) had prior treatment for TB. The median X-ray score was 0.63 (interquartile range 0.14-0.91), and 153 (55%) had a score above 0.5 (the manufacturer-recommended threshold). TB was diagnosed in 131 patients (47%), including 76 with bacteriological confirmation. CAD had an area under the curve (AUC) of 0.65 (95% confidence interval 0.58–0.71) using a composite definition and 0.65 (0.57–0.73) for bacteriologically confirmed TB. An X-ray score threshold of 0.5 had sensitivity and specificity of 66% and 54%, respectively, for bacteriologically confirmed TB. Lowering the threshold to 0.2 increased sensitivity to 80% but decreased specificity to 37%. The AUC of CAD was higher among patients without prior TB treatment and among those without HIV infection (Figure 1). Conclusion Chest x-ray with CAD could serve as a supplementary tool for diagnosing TB in adults with Xpert trace-positive sputum, particularly those without prior tuberculosis or HIV infection. However, a low X-ray score did not reliably rule out TB disease. Disclosures Paul K. Drain, MD, MPH, Abbott Diagnostics: Advisor/Consultant|Abbott Diagnostics: Grant/Research Support|Giner: Advisor/Consultant|OraSure: Advisor/Consultant|Revvity: Advisor/Consultant|Revvity: Grant/Research Support|Roche: Advisor/Consultant|Roche: Grant/Research Support

Open Forum Infectious DiseasesVol. 13(Supplement_1)
Johns Hopkins University (US), University of Washington (US), Johns Hopkins Medicine (US), Edendale Hospital (ZA), Grey's Hospital (ZA), Makerere University (UG)
Good health and well-being
Openalex Percentile: Top 92%
COVID-19 diagnosis using AI
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