Chest radiographs and machine learning - Past, present and future

Catherine M Jones, Quinlan D Buchlak, Luke Oakden-Rayner, Michael Milne, Jarrel Seah, Nazanin Esmaili, Ben Hachey, Catherine M Jones, Quinlan D Buchlak, Luke Oakden-Rayner, Michael Milne, Jarrel Seah, Nazanin Esmaili, Ben Hachey

Abstract

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed.

Keywords: chest X-ray; clinical decision support; deep learning; machine learning; radiomics.

© 2021 Annalise-AI. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists.

Figures

Fig. 1
Fig. 1
(a) Heatmap investigating an exemplar algorithm in classifying pneumothorax, demonstrating its focus on the right apical pneumothorax rather than the right‐sided intercostal drain. (b) Original image demonstrating right apical pneumothorax.

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