ArticleThe Journal of infectious diseases2026
Tuberculosis Disease Severity Assessment Using Clinical Variables and Radiology Enabled by Artificial Intelligence.
Article in The Journal of infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Within-host pathogen population diversity predicts treatment response in Tuberculosis.medRxiv : the preprint server for health sciences · 2026Article
- Insights into the Role of Rifampicin Exposure and Clinical Baseline Covariates on the Response to Pulmonary Tuberculosis Treatment.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response.Nature communications · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundChest X-ray (CXR) can assess pulmonary tuberculosis (TB) severity and may guide duration of treatment. However, the optimal radiological metric and its integration with clinical variables for predicting treatment outcomes remains unclear.
methodsWe used logistic regression to associate human-read and commercial artificial intelligence-generated CXR metrics with unfavorable outcome in the TB Portals real-world dataset (n = 2809). We assessed the standalone predictive accuracy for each of 10 radiological features for unfavorable outcomes, and combined the best-performing features with other clinical data. We fine-tuned ensembles of convolutional neural nets (CNNs) to automate human-read percent of lung involved (PLI) measurement directly from CXR images (n = 5261).
resultsHuman-read PLI is the only CXR finding associated with outcome across drug resistance and human immunodeficiency virus (HIV) subgroups and is optimally combined with age, sex, and smear grade for predicting treatment outcome. PLI predicts outcomes better than cavitation (area under the curve [AUC], 0.654 vs 0.581, respectively), performs better than all tested Qure.ai automated features (qXR v2), and improves outcome prediction when added to sex + age + smear grade (ΔAUC, 0.028 [95% confidence interval, .007-.042]). The CNN ensemble for predicting PLI >25% achieves an AUC of 0.850.
conclusionsPLI performs better than cavitation as a radiological marker for predicting TB treatment outcome, and improves risk stratification when combined with key clinical variables. Automation of PLI can be predicted using CNNs to enable scalability and accurate assessment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.