• Mona El-Gedaily, André Euler, Mike Guldimann, Bastian Schulz, Foroud Aghapour Zangeneh, Andreas Prause, Rahel A Kubik-Huch, and Tilo Niemann.
• Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
• Medicine (Baltimore). 2024 Nov 22; 103 (47): e40485e40485.

AbstractThe aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist's diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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