Artificial intelligence for computer aided detection of pneumoconiosis: A succinct review since 1974

Abstract

With the increasing availability of medical imaging data and advancements in artificial intelligence (AI) and computer vision (CV) techniques, computer aided diagnostic (CAD) systems have been consistently developed to help radiologists in the detection of pneumoconiosis. Pneumoconiosis is a respiratory disease caused by long term exposure of industrial dust. Pneumoconiosis has remained prevalent, even in countries with sophisticated healthcare systems and strict workplace measures. Consequently, this review article focuses on cascading the literature of existing CAD systems for pneumoconiosis diagnosis since 1974, providing a baseline for future research in this domain. For this purpose, 58 relevant articles were first selected after employing strict inclusion criteria, through 10 reliable databases and search engines, including Scopus, IEEE, Google Scholar, and PubMed etc. This review then systematically categorizes the selected CAD studies into two patterns, based on the employed methodology for pneumoconiosis diagnosis: texture-based and non-texture-based CAD systems. This study reveals that texture-based methods have been extensively adopted for pneumoconiosis diagnosis compared to non-texture-based methods (or deep learning based). However, deep learning approaches have shown superior performance thanks to the recent availability of large annotated CXR datasets and development of deep convolutional neural network (CNN) and transformer-based architectures. Finally, the analysis is concluded with a discussion on the shortcomings of current CAD systems and some suggested future directions for the development of effective diagnostic systems. Additionally, a number of benchmark datasets have also been discussed.