Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Abstract

Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for the detection of thoracic pathologies. Remarkable research is being carried out to propose robust automatic diagnostic tools for the detection of pathologies from the chest radiographs. Artificial Intelligence techniques have been found to give promising results in automating the field of medicine. Lot of research has been done for automatic detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence techniques. Undoubtedly, several models are available for automatic diagnosis of pneumothorax, however a summarized review of the existing literature is still missing. This study summarizes the existing literature for pneumothorax detection from chest x-rays along with describing the available chest radiographs datasets. It will help the researchers to select the optimal and most effective model with respect to the real-time scenarios. The comparative analysis of the literature is provided in terms of goodness, usability, and quality along with highlighting the research gaps for further investigation. From the literature, it is evident that pneumothorax is more common in men as compared to women. Additionally, the proposed models have achieved incredible results for pneumothorax detection on selected datasets, however, the effectiveness of proposed models in real-time cases cannot be claimed, as none of the models have been implemented clinically yet. This issue can be solved by external validation of the models. Furthermore, the class-imbalance problem in most of the medical image dataset has been solved by algorithm-level-techniques. Moreover, there is need to put more effort in combined detection and localization of pneumothorax as mostly research is limited to either classification or localization of pathology. So far, best results have been achieved by deep-learning based models with Area-under-receiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of 88.21% for localization of pneumothorax. Thus, the outstanding abilities of deep learning techniques can be deployed for developing robust models for pneumothorax detection.

Keywords

• Chest radiographs
• chest x-rays datasets
• class-imbalance
• deep learning
• ensemble
• machine learning