The Role of Artificial Intelligence in Improving Diagnostic Accuracy for Paediatric Tuberculosis - Sharma, Kanisheka; Adhikary, Arindam; Alam, Inkashaf; Sawhney, Shagun (Journal of Neonatal Surgery, 2025)
Despite worldwide efforts focused on early diagnosis and treatments to reduce disease transmission, Tuberculosis (TB) remain the leading cause of death for children. Childhood tuberculosis diagnosis is complicated due to the fact that the disease is usually paucibacillary and that it is difficult to collect appropriate clinical samples, leading to the poor sensitivity of the existing tests based on pathogens. Over the last few years, significant technological changes in particular the creation of artificial intelligence (AI) and deep learning have started to transform the field of pediatric TB diagnosis and management. AI-enhance digital chest radiography has demonstrated diagnostic power and sensitivity similar to expert radiologists and at the same time eliminates workload, inter-reader reliability and delays in diagnostics. The CAD systems based on machine learning enhance finding radiographic abnormalities at an early stage and it can be particularly applicable to tuberculosis screening in low-resource or high-burden cases. The review defines the current diagnostic methods like Microscopy, Culture, NAAT like Xpert Mycobacterium tuberculosis/RIF, Interferon-gamma release assays (IGRA) and advanced imaging modalities that emphasize the necessity of having integrated, high-precise and cost-effective diagnostics. Although the potential of AI-based methods is obvious, the issue of data privacy and the absence of standardized validation research, which ensure equal access to AI-based methods, as well as effective systems of governance, are vital measures toward achieving the full potential of artificial intelligence in pediatric TB care.We also the primary challenges to applying AI for paediatric tuberculosis diagnosis and look at current AI application for TB screening in resource-constrained situations. Overall, this review highlights that AI is an effective supplement to current diagnostics, which have promising possibilities of earlier diagnosis, more accurate screening and better management of TB in children worldwide