ReviewFrontiers in medicine2022
The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.
Review in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Recent advances in antituberculosis drugs and therapeutics: from mechanistic breakthroughs to clinical translation.Molecular biomedicine · 2026Review
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Biofilm Associated Persistence and Drug Tolerance in Mycobacteria Within Host Microenvironments.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
- Radiomic analysis of postmortem lung changes: a PMCT-based approach for estimating the postmortem interval.Forensic science, medicine, and pathology · 2026Article
- Article
- Beyond accuracy: evaluating the operational feasibility and diagnostic yield of CAD4TB vs. Timika score for scalable TB screening in low-resource settings.Frontiers in digital health · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Article
- Artificial intelligence for tuberculosis management in Africa: opportunities, challenges, and implementation.Frontiers in public health · 2026Review
- Development of machine learning models to identify potentially active compounds against tuberculosis.Scientific reports · 2025Article
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- Epithelial Cell Dysfunction in Pulmonary Fibrosis: Mechanisms, Interactions, and Emerging Therapeutic Targets.Pharmaceuticals (Basel, Switzerland) · 2025Review
- How could emerging nanomedicine-based tuberculosis treatments outperform conventional approaches?Nanomedicine (London, England) · 2025Article
- The next generation of drug resistant tuberculosis drug design.Future medicinal chemistry · 2025Article
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- Prediction of active drug-resistant pulmonary tuberculosis based on CT radiomics: construction and validation of independent models and combined models for residual pulmonary parenchyma.Frontiers in medicine · 2025Article
- Cost-effectiveness analysis of AI-assisted chest X-ray interpretation tools for TB screening: a rapid HTA.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.
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Registered trials
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 OpenQuestion graph.