Related Experiment Video
Updated: Jan 14, 2026
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
Published on: October 13, 2023
Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis
Daniel Capellán-Martín1,2,3, Juan J Gómez-Valverde4,5, Ramón Sánchez-Jacob6,7
1Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain. daniel.capellan@upm.es.
Insights
A new deep learning framework, pTBLightNet, effectively detects pediatric tuberculosis (TB) using chest X-rays (CXRs). This tool shows promise for improving TB diagnosis in children, especially in resource-limited settings.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Pediatric Infectious Diseases
- Radiology and Imaging Analysis
Background:
- Tuberculosis (TB) is a significant global health issue, with pediatric TB diagnosis being particularly challenging due to non-specific symptoms and subtle radiological findings.
- Many children with TB remain undiagnosed or untreated, highlighting the need for improved diagnostic tools.
- The World Health Organization (WHO) recommends chest X-rays (CXRs) for TB screening due to their accessibility and rapid assessment capabilities.
Purpose of the Study:
- To develop and evaluate pTBLightNet, a multi-view deep learning framework for detecting pediatric pulmonary TB from CXRs.
- To assess the framework's performance across different age groups and its generalizability using various reference standards.
- To explore the impact of incorporating lateral CXR views on diagnostic accuracy in children.
Main Methods:
- Developed pTBLightNet, a multi-view deep learning framework utilizing both frontal and lateral CXR views.
- Pre-trained the model on a large adult CXR dataset (N=114,173) and fine-tuned/trained it on pediatric TB cohorts (N=918).
- Evaluated the framework's performance using internal and external testing, comparing against CXR TB compatibility, expert readings, microbiological confirmation, and case definitions.
Main Results:
- Achieved an area under the curve (AUC) of 0.903 on internal testing and 0.682 on external testing.
- External evaluation demonstrated effectiveness and generalizability across diverse reference standards.
- Age-specific models (<5 and 5-18 years) performed competitively, with lateral CXRs improving diagnosis in younger children.
Conclusions:
- pTBLightNet demonstrates robustness and potential for improving pediatric TB diagnosis, particularly in resource-limited settings.
- The framework's ability to leverage multi-view CXRs and age-specific models enhances its diagnostic utility.
- This deep learning approach offers a promising avenue for addressing the challenges in pediatric TB detection.
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 (<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.
More Related Videos
Related Concept Videos
01:28Pulmonary Tuberculosis II
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
01:31Pulmonary Tuberculosis III
The first classification is based on the development of the disease, and it includes the following categories:
01:28Pulmonary Tuberculosis V
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
01:29Pulmonary Tuberculosis I
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
01:26Pulmonary Tuberculosis IV
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...