Deep-learning based computer aided diagnosis system in detecting fractures on anteroposterior chest DR films
10.13929/j.issn.1672-8475.2020.11.009
- VernacularTitle: 基于深度学习的计算机辅助诊断系统检出DR胸部正位片中的骨折
- Author:
Yuxin WU
1
Author Information
1. Graduate School, Fujian Medical University
- Publication Type:Journal Article
- Keywords:
Computer aided diagnosis system;
Deep learning;
Fluoroscopy;
Fractures, bone;
Radiography, thoracic
- From:
Chinese Journal of Interventional Imaging and Therapy
2020;17(11):675-678
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To evaluate the efficiency of deep-learning based computer aided diagnosis system (DL-CAD) in detecting fractures on DR chest anteroposterior films, and to explore its capability of assisting the junior radiologist. Methods: ①Experiment 1: A total of 547 DR chest anteroposterior films, including 361 patients with 983 chest fractures and 186 without chest fractures were retrospectively analyzed. The predictive performance of DL-CAD for fracture was evaluated. ②Experiment 2: Totally 397 patients were randomly selected from experiment 1, including 211 cases with 604 chest fractures and 186 cases without chest fractures. The results of DL-CAD alone (group 1), a junior radiology resident alone (group 2), a junior radiology resident aided with DL-CAD (group 3) and a senior radiologist alone (group 4) were recorded and compared, respectively. Results: ①For experiment 1: Among 983 fractures, DL-CAD identified 672 fractures, 641 were correctly identified and 31 were misdiagnosed, with a sensitivity of 65.21% (641/983) and F-measure of 77.46%. Out of a total of 361 fracture cases, DL-CAD identified 314 cases, misdiagnosed 6 cases, with a sensitivity of 86.98% (314/361) and F-measure of 92.22%. ②Experiment 2: The sensitivity of fracture detection was 62.09% (375/604), 61.59% (372/604), 86.75% (524/604) and 83.44% (504/604), and the F-measure was 75.38%, 74.62%, 90.74%, 89.84% for group 1, 2, 3 and 4, respectively. The detection efficacy of group 3 and 4 were both higher than that of group 1 and 2 (all P<0.001). There was no significant difference between group 1 and group 2, nor group 3 and group 4 (both P>0.05). Conclusion: DL-CAD software showed good detection effect of fractures on DR chest anteroposterior films, which could effectively improve the diagnostic performance of junior radiologist in fracture detection.
