Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis

TL;DR

Artificial intelligence is being explored to help diagnose pediatric pulmonary tuberculosis, but challenges like limited data and unclear clinical applications remain.

Contribution

This paper systematically reviews AI applications in pediatric pulmonary tuberculosis diagnosis and proposes future directions for collaborative datasets and explainable AI.

Findings

AI technologies like deep learning are being applied to medical imaging and diagnostics for tuberculosis.

Most AI research focuses on adult cases, with limited studies on pediatric pulmonary tuberculosis.

Challenges include data scarcity, model interpretability, and lack of clinical validation for pediatric AI tools.

Abstract

Tuberculosis (TB) is a serious disease that poses a significant threat to the health of children and adolescents, with pulmonary tuberculosis (PTB) being the most common type. Due to the lack of specificity in clinical manifestations and symptoms, early screening and diagnosis of pediatric pulmonary tuberculosis present significant challenges. In recent years, the artificial intelligence (AI) healthcare industry has emerged as a major driving force for transformation in the global healthcare sector. Through technologies such as deep learning, natural language processing, computer vision and multimodal fusion, intelligent solutions are brought to medical links such as clinical auxiliary diagnosis. The combination mode of AI with medical imaging, laboratory diagnosis, pathology examination and other data has also been gradually applied to tuberculosis screening and diagnosis. However, its development is constrained by bottlenecks such as the scarcity of high-quality data on children, insufficient interpretability of models, lack of external validation, and unclear clinical translation paths. Moreover, most of the existing related studies focus on adult pulmonary tuberculosis, and there is a lack of sufficient research and reporting on pediatric pulmonary tuberculosis. This article aims to systematically review the research and application status of AI in the auxiliary diagnosis of pediatric pulmonary tuberculosis in recent years, critically analyze the current limitations, and explore that in the future, efforts should be made to build cross-institutional and multi-center collaborative datasets and carry out explainable AI verification centered on clinical efficacy. Explore the development path of the application of AI in the full-chain management of “prevention—diagnosis—treatment—management” of pediatric pulmonary tuberculosis.

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