ReviewPediatric radiology2023
Artificial Intelligence in Paediatric Tuberculosis.
Review in Pediatric radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Triage and workflow optimization with artificial intelligence in pediatric imaging.Pediatric radiology · 2026Review
- Pediatric Tuberculosis: Unraveling Immunity, Clinical Complexities, and Resource-Driven Disparities in the Pursuit of Prevention.Vaccines · 2026Review
- From gaps to progress: five-year global advances in the diagnosis of pediatric tuberculosis.Frontiers in pediatrics · 2026Review
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- Guiding responsible AI in healthcare in the Philippines.NPJ digital medicine · 2025Review
- Closing the gaps in childhood TB detection.IJTLD open · 2025Article
- Mobile application prototyping using Artificial Intelligence to support childhood tuberculosis diagnosis.Revista brasileira de enfermagem · 2025Article
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- Diagnostic performance of chest radiography for pediatric tuberculosis across high- and low-burden settings.Frontiers in pediatrics · 2025Article
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Intraocular tuberculosis masquerading as ocular tumor: A case report.Radiology case reports · 2024Article
- Current and evolving directions in childhood tuberculosis imaging.Pediatric radiology · 2024Article
- Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.Journal of pharmaceutical analysis · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.
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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.