Oloko-Oba Mustapha, Viriri Serestina
Computer Science Discipline, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Front Med (Lausanne). 2022 Mar 10;9:830515. doi: 10.3389/fmed.2022.830515. eCollection 2022.
The high mortality rate in Tuberculosis (TB) burden regions has increased significantly in the last decades. Despite the possibility of treatment for TB, high burden regions still suffer inadequate screening tools, which result in diagnostic delay and misdiagnosis. These challenges have led to the development of Computer-Aided Diagnostic (CAD) system to detect TB automatically. There are several ways of screening for TB, but Chest X-Ray (CXR) is more prominent and recommended due to its high sensitivity in detecting lung abnormalities. This paper presents the results of a systematic review based on PRISMA procedures that investigate state-of-the-art Deep Learning techniques for screening pulmonary abnormalities related to TB. The systematic review was conducted using an extensive selection of scientific databases as reference sources that grant access to distinctive articles in the field. Four scientific databases were searched to retrieve related articles. Inclusion and exclusion criteria were defined and applied to each article to determine those included in the study. Out of the 489 articles retrieved, 62 were included. Based on the findings in this review, we conclude that CAD systems are promising in tackling the challenges of the TB epidemic and made recommendations for improvement in future studies.
在过去几十年中，结核病（TB）负担较重地区的高死亡率显著上升。尽管结核病有治疗的可能性，但高负担地区仍缺乏足够的筛查工具，这导致诊断延迟和误诊。这些挑战促使了计算机辅助诊断（CAD）系统的发展，以自动检测结核病。结核病有多种筛查方式，但胸部X光（CXR）因其在检测肺部异常方面的高敏感性而更为突出且被推荐。本文展示了基于PRISMA程序的系统评价结果，该评价调查了用于筛查与结核病相关的肺部异常的前沿深度学习技术。该系统评价使用了广泛选择的科学数据库作为参考来源，这些数据库可获取该领域的独特文章。搜索了四个科学数据库以检索相关文章。定义了纳入和排除标准，并应用于每篇文章以确定纳入研究的文章。在检索到的489篇文章中，有62篇被纳入。基于本评价的结果，我们得出结论，CAD系统在应对结核病流行的挑战方面很有前景，并对未来研究的改进提出了建议。
