• Masafumi Kaiume, Shigeru Suzuki, Koichiro Yasaka, Haruto Sugawara, Yun Shen, Yoshiaki Katada, Takuya Ishikawa, Rika Fukui, and Osamu Abe.
• Department of Radiology, Tokyo Women's Medical University Medical Center East, Arakawa-ku.
• Medicine (Baltimore). 2021 May 21; 100 (20): e26024e26024.

AbstractTo evaluate the rib fracture detection performance in computed tomography (CT) images using a software based on a deep convolutional neural network (DCNN) and compare it with the rib fracture diagnostic performance of doctors.We included CT images from 39 patients with thoracic injuries who underwent CT scans. In these images, 256 rib fractures were detected by two radiologists. This result was defined as the gold standard. The performances of rib fracture detection by the software and two interns were compared via the McNemar test and the jackknife alternative free-response receiver operating characteristic (JAFROC) analysis.The sensitivity of the DCNN software was significantly higher than those of both Intern A (0.645 vs 0.313; P < .001) and Intern B (0.645 vs 0.258; P < .001). Based on the JAFROC analysis, the differences in the figure-of-merits between the results obtained via the DCNN software and those by Interns A and B were 0.057 (95% confidence interval: -0.081, 0.195) and 0.071 (-0.082, 0.224), respectively. As the non-inferiority margin was set to -0.10, the DCNN software is non-inferior to the rib fracture detection performed by both interns.In the detection of rib fractures, detection by the DCNN software could be an alternative to the interpretation performed by doctors who do not have intensive training experience in image interpretation.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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