Dataset Information

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

ABSTRACT:

Background

Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH).

Methods

We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy.

Results

We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7-61.9]; Lunit, 54.1% [95% CI: 44.6-63.3]; qXRv2, 60.5% [95% CI: 51.7-68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, -13.4% [-21.1, -6.9]; Lunit, +2.2% [-3.6, +6.3]; qXRv2: -13.4% [-21.5, -6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, -12.3% [-19.5, -6.1]; Lunit, -17.2% [-24.6, -10.5]; qXRv2, -16.6% [-24.4, -9.9]. Accuracy was similar to human readers.

Conclusions

For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.

SUBMITTER: Tavaziva G

PROVIDER: S-EPMC9049274 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

Tavaziva Gamuchirai G Harris Miriam M Abidi Syed K SK Geric Coralie C Breuninger Marianne M Dheda Keertan K Esmail Aliasgar A Muyoyeta Monde M Reither Klaus K Majidulla Arman A Khan Aamir J AJ Campbell Jonathon R JR David Pierre-Marie PM Denkinger Claudia C Miller Cecily C Nathavitharana Ruvandhi R Pai Madhukar M Benedetti Andrea A Ahmad Khan Faiz F

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20220401 8

<h4>Background</h4>Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH).<h4>Methods</h4>We collected CXRs and individual patient data (IPD) from studies evaluating CAD in p ...[more]

PMID: 34286831

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Project description:BackgroundIn application studies of latent class analysis (LCA) evaluating imperfect diagnostic tests, residual dependence among the diagnostic tests still remain even after conditioning on the true disease status due to measured variables known to affect prevalence and/or alter diagnostic test accuracy. Presence of severe comorbidities such as HIV in pulmonary tuberculosis (PTB) diagnosis alter the prevalence of PTB and affect the diagnostic performance of the available imperfect tests in use. This violates two key assumptions of LCA: (1) that the diagnostic tests are independent conditional on the true disease status (2) that the sensitivity and specificity remain constant across subpopulations. This leads to incorrect inferences.MethodsThrough simulation we examined implications of likely model violations on estimation of prevalence, sensitivity and specificity among passive case-finding presumptive PTB patients with or without HIV. Jointly conditioning on PTB and HIV, we generated independent results for five diagnostic tests and analyzed using Bayesian LCA with Probit regression, separately for sets of five and three diagnostic tests using four working models allowing: (1) constant PTB prevalence and diagnostic accuracy (2) varying PTB prevalence but constant diagnostic accuracy (3) constant PTB prevalence but varying diagnostic accuracy (4) varying PTB prevalence and diagnostic accuracy across HIV subpopulations. Vague Gaussian priors with mean 1 and unknown variance were assigned to the model parameters with unknown variance assigned Inverse Gamma prior.ResultsModels accounting for heterogeneity in diagnostic accuracy produced consistent estimates while the model ignoring it produces biased estimates. The model ignoring heterogeneity in PTB prevalence only is less problematic. With five diagnostic tests, the model assuming homogenous population is robust to violation of the assumptions.ConclusionWell-chosen covariate-specific adaptations of the model can avoid bias implied by recognized heterogeneity in PTB patient populations generating otherwise dependent test results in LCA.

Dataset Information

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

Background

Methods

Results

Conclusions

Publications

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

Similar Datasets

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