Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy

Abstract

Background: Three and half million anonymous X-rays were gathered from 45 locations worldwide (in-hospital and outpatient settings). qXR was initially trained on this massive dataset. We used an independent dataset of 13,426 chest X-rays from radiologists’ reports. The test data set included 213,459 X-rays chosen at random from a pool of 3.5 million X-rays. The dataset (development) was developed using the remaining X-rays received from the remaining patients. Methods: qXR is a deep learning algorithm-enabled software that is used to study nodules and malignant nodules on X-rays. We observed moderate to a substantial agreement even when observations were made with normal X-rays. Results: qXR presented a high area under the curve (AUC) of 0.99 with a 95% confidence interval calculated with the Clopper–Pearson method. The specificity obtained with qXR was 0.90, and the sensitivity was 1 at the operating threshold. The sensitivity value of qXR in detecting nodules was 0.99, and the specificity ranged from 0.87 to 0.92, with AUC ranging between 0.98 and 0.99. The malignant nodules were detected with a sensitivity ranging from 0.95 to 1.00, specificity between 0.96 and 0.99, and AUC from 0.99 to 1. The sensitivity of radiologists 1 and 2 was between 0.74 and 0.76, with a specificity ranging from 0.98 to 0.99. In detecting the malignant nodules, specificity ranged between 0.98 and 0.99, and sensitivity fell between 0.88 and 0.94. Conclusion: Machine learning model can be used as a passive tool to find incidental cases of lung cancer or as a triaging tool, which accelerate the patient journey through standard care pipeline for lung cancer.

Keywords

• artificial intelligence
• chest x-rays
• convolutional neural network
• deep learning
• malignant nodules