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.

Intraocular tuberculosis masquerading as ocular tumor: A case report

“…It is mainly spread when infected individuals expel the bacteria via aerosols [7] . It is a leading infectious cause of morbidity and mortality in children worldwide and a major health concern, particularly in Africa and Southeast Asia, where the highest number of children are affected [8] . Children and young adolescents aged under 15 years represent around 11% of all TB cases globally, with 1.1 million new TB infections every year in this age group [9] .…”

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

“…The data-driven approach could subsequently generate individualized risk profiles supported by a high level of computing capacity, enabling clinicians to tailor treatment strategies accordingly. For instance, the integration of AI technologies with radiological imaging has shown promise in improving TB detection and diagnosis based on large datasets of chest X-rays and computed tomography scans [ [155] , [156] , [157] , [158] ]. Not limited to the host, but able to expand to the microorganism, these algorithms could provide a framework for identifying and predicting TB drug-resistant pathogens [ 159 ].…”

“…The benefits of machine learning and AI in TB management are the potential directions to establish accurate predictive models. For instance, predicting pharmacometrics and optimizing the dosing of Lfx [ 170 ], and screening computer-assisted diagnosis in pulmonary imaging [ 157 , 158 ], which could be embedded into the EHR system. The challenges of clinical medicine in general and untargeted metabolomics studies, in particular, could be demolished with the advancement of AI, machine learning, and bioinformatic methods [ 171 , 172 ].…”

Current and evolving directions in childhood tuberculosis imaging

“…A team of pediatric radiologists led by Nasreen Mahomed present a comprehensive review depicting the classic imaging findings of childhood TB in various parts of the body [7], while Elsingergy and coauthors compare chest radiograph findings in ambulatory and hospitalized children with pulmonary TB [8]. Also included in the mini-symposium are articles on the use of artificial intelligence in pediatric TB by Jaishree Naidoo and colleagues [9] as well as information on global resources available in the fight against TB, by Joanna Kasznia-Brown [10]. This topic has not been covered before and brings new promising aspects in the fight against TB.…”