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

TL;DR

This systematic review summarizes AI-based methods for automatic pneumothorax detection from chest radiographs, highlighting datasets, performance, limitations, and research gaps to guide future advancements.

Contribution

It compiles and compares existing AI techniques for pneumothorax detection, identifies research gaps, and provides guidance for selecting optimal approaches in future studies.

Findings

AI techniques show promising detection accuracy.

Several datasets are available for training models.

Research gaps include dataset diversity and model robustness.

Abstract

Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for detection of thoracic pathologies. Research is being carried out in order to propose robust automatic diagnostic tool for detection of pathologies from chest radiographs. Artificial Intelligence techniques especially deep learning methodologies have found to be giving promising results in automating the field of medicine. Lot of research has been done for automatic and fast detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence and machine learning techniques. This study summarizes the existing literature for the automatic detection of pneumothorax from chest x-rays along with describing the available chest radiographs datasets. The comparative analysis of the literature is also provided in terms of goodness. Limitations of the existing literature along with the research gaps is also given for further investigation. The paper provides a brief overview of the present work for pneumothorax detection for helping the researchers in selection of optimal approach for future research.