Artificial Intelligence in Paediatric Tuberculosis

Abstract

Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.

Keywords: Artificial intelligence; Chest radiography; Children; Computer aided detection; Deep learning; Tuberculosis.

Fig. 1

A simplified view of how artificial neural networks function. Although 2 layers are shown, hundreds or thousands of layers are used

Fig. 2

a, b AP chest radiograph in a 2-year-old girl with pulmonary tuberculosis. a Before use of the artificial intelligence (AI) tool. b The AI tool identifies lymphadenopathy (two smallest bounding boxes) and consolidation (two largest bounding boxes) and measures the cardiothoracic ratio (horizontal lines)