Deep learning helps radiologists detect lung cancer on chest X-rays – Physics World

Chest radiography is the most common imaging exam used for lung cancer screening. However, the size, density and location of lung lesions make their detection on chest X-rays challenging. Recently, machine-learning methods have been developed to help improve diagnostic accuracy, with deep convolutional neural networks (DCNNs), showing promise for chest radiograph interpretation. A study from four medical centres on three continents has now demonstrated that DCNN software can improve radiologists' detection of malignant lung cancers on chest X-rays (Radiology 10.1148/radiol.2019182465). "The average sensitivity of radiologists was improved by 5.2% when they re-reviewed X-rays with the deep-learning software," says Byoung Wook Choi from Yonsei University College of Medicine in Seoul, Korea.