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

Daniel Capellán-Martín (), Juan J. Gómez-Valverde (), Ramón Sánchez-Jacob, Alicia Hernanz-Lobo, H. Simon Schaaf, Lara García-Delgado, Orvalho Augusto, Pooneh Roshanitabrizi, Alberto L. García-Basteiro, Jose Luis Ribó, Ángel Lancharro, Antoni Noguera-Julian, Daniel Blázquez-Gamero, Marius George Linguraru, Begoña Santiago-García, Elisa López-Varela and María J. Ledesma-Carbayo ()
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Daniel Capellán-Martín: Universidad Politécnica de Madrid
Juan J. Gómez-Valverde: Universidad Politécnica de Madrid
Ramón Sánchez-Jacob: Children’s National Hospital
Alicia Hernanz-Lobo: Gregorio Marañón University Hospital
H. Simon Schaaf: Stellenbosch University
Lara García-Delgado: Universidad Politécnica de Madrid
Orvalho Augusto: University of Washington
Pooneh Roshanitabrizi: Children’s National Hospital
Alberto L. García-Basteiro: Instituto de Salud Carlos III
Jose Luis Ribó: Hospital Universitari General de Catalunya
Ángel Lancharro: Gregorio Marañón Research Health Institute (IiSGM)
Antoni Noguera-Julian: RITIP Translational Research Network in Pediatric Infectious Diseases
Daniel Blázquez-Gamero: Instituto de Salud Carlos III
Marius George Linguraru: Children’s National Hospital
Begoña Santiago-García: Gregorio Marañón University Hospital
Elisa López-Varela: Centro de Investigação em Saúde de Manhiça
María J. Ledesma-Carbayo: Universidad Politécnica de Madrid

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract 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 (

Date: 2025
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DOI: 10.1038/s41467-025-64391-1

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