Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis

Tuberculosis (TB) remains a major global health burden, particularly in low-resource, high-prevalence regions. Pediatric TB diagnosis poses challenges with non-specific symptoms and less distinct radiological manifestations than adult TB. Many affected children remain undiagnosed or untreated. The W...

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Vydáno v:Nature communications Ročník 16; číslo 1; s. 9170 - 16
Hlavní autoři: Capellán-Martín, Daniel, Gómez-Valverde, Juan J., Sánchez-Jacob, Ramón, Hernanz-Lobo, Alicia, Schaaf, H. Simon, García-Delgado, Lara, Augusto, Orvalho, Roshanitabrizi, Pooneh, García-Basteiro, Alberto L., Ribó, Jose Luis, Lancharro, Ángel, Noguera-Julian, Antoni, Blázquez-Gamero, Daniel, Linguraru, Marius George, Santiago-García, Begoña, López-Varela, Elisa, Ledesma-Carbayo, María J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 27.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166/985
639/705/117
692/308/3187
692/699/255/1856
Abnormalities
Adolescent
Age
Age groups
Artificial intelligence
Assessments
Chest
Child
Child, Preschool
Classification
Clinical medicine
Compatibility
Coronaviruses
Datasets
Deep Learning
Diagnosis
Disease
Female
Global health
Humanities and Social Sciences
Humans
Infant
Male
Medical diagnosis
multidisciplinary
Pandemics
Pediatrics
Public health
Radiography, Thoracic - methods
Science
Science (multidisciplinary)
Tuberculosis
Tuberculosis, Pulmonary - diagnosis
Tuberculosis, Pulmonary - diagnostic imaging
X-rays
ISSN:2041-1723, 2041-1723
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Shrnutí:Tuberculosis (TB) remains a major global health burden, particularly in low-resource, high-prevalence regions. Pediatric TB diagnosis poses challenges with non-specific symptoms and less distinct radiological manifestations than adult TB. Many affected children remain undiagnosed or untreated. The World Health Organization (WHO) recommends chest X-ray (CXR) for TB screening and triage, given its accessibility and rapid assessment of pulmonary TB-related abnormalities. We present pTBLightNet, a multi-view deep learning framework to detect pediatric pulmonary TB by identifying TB-compatible CXRs with consistent radiological findings. Leveraging both frontal and lateral CXR views, our framework is pre-trained on adult CXR datasets (N = 114,173), then fine-tuned or trained from scratch, and subsequently evaluated on CXR datasets (N = 918) from three pediatric TB cohorts. It achieves an area under the curve (AUC) of 0.903 and 0.682 on internal and external testing, respectively. External evaluation supports its effectiveness and generalizability using CXR TB compatibility, expert reading, microbiological confirmation and case definition as reference standards. Age-specific models (<5 and 5–18 years) perform competitively with those trained on larger undifferentiated populations, and adding lateral CXRs improves diagnosis in younger children. These results highlight the robustness of our approach across age groups and its potential to improve TB diagnosis, particularly in resource-limited settings. In this work, authors present an AI framework to detect pediatric tuberculosis by using both frontal and lateral chest X-rays, showing robust performance across several age groups and settings.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-64391-1