Elsevier

Academic Radiology

Volume 27, Issue 1, January 2020, Pages 71-75
Academic Radiology

Special Review
Artificial Intelligence, Radiology, and Tuberculosis: A Review

https://doi.org/10.1016/j.acra.2019.10.003Get rights and content

Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.

Section snippets

INTRODUCTION

Tuberculosis (TB) is an epidemic in many parts of the world, being responsible for 1.6 million deaths in 2017; in the same year, 10 million people developed the disease (1). TB is the leading cause of death from infectious disease worldwide and disproportionately affects developing regions, such as Africa and South-East Asia (1). Frequently, countries that suffer from a high TB prevalence are also resource-poor, lacking in medical staff and equipment.

Perhaps the most pressing issue relating to

CHEST RADIOGRAPHY AND TB

The radiographic presentation of pulmonary TB is varied, making it a challenging diagnosis. Firstly, it is important to distinguish between active and latent TB. Active TB is characterized by the presence of consolidation and cavitary lesions in the lungs, and has a high risk of infectious spread (2). By contrast, latent TB is characterized by stable fibronodular changes, such as scarring and nodular opacification, and has a low risk of infectious spread (although the disease can be reactivated

COMPUTER-AIDED DIAGNOSIS

In its early development, TB detection using AI was studied with conventional CAD (8). In CAD systems, detection is performed using a manually created preset feature model and the computer does not “learn” given a greater caseload. As a result, it has proved difficult to incorporate the varied presentation of TB into a single CAD system. CAD systems developed from 1996 to 2013 achieved accuracies ranging from 42% to 100%, usually for specific features such as the presence of cavitations (8).

DEEP LEARNING

Recently, AI deep learning networks have been developed for TB detection. The first of these was developed by Hwang et al. in 2016 (18). The authors used a model called AlexNet, a pretrained deep learning network that had previously achieved success in the ImageNet Large Scale Visual Recognition Competition—an image classification challenge for nonmedical images. AlexNet had already been pretrained for image recognition—this capability merely needed to be adjusted for use in medical imaging, as

TOWARD REFINEMENT IN DEEP LEARNING

Most recently, models for TB detection are becoming more refined and streamlined. Previously described deep learning models, such as AlexNet and GoogLeNet, are pretrained on millions of images and are capable of distinguishing between thousands of classes of images even before they are used in the context of radiology. As a consequence, they require substantial computer memory and hardware requirements to function effectively, even when working on the narrow task of detecting TB on a chest

CONCLUSION

The use of AI to detect TB in chest radiographs has progressed significantly in the past 3 decades. The field began with the development of CAD programs, which showed promising results for limited narrow applications. However, these tools were limited by their small dataset size and their reliance on preset feature models, a fundamental weakness of the technology itself. In the last 5 years, deep learning has made rapid progress in medical imaging, with TB detection emerging as an area of

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