AUTHOR=Liu Pengran , Lu Lin , Chen Yufei , Huo Tongtong , Xue Mingdi , Wang Honglin , Fang Ying , Xie Yi , Xie Mao , Ye Zhewei 

TITLE=Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.927926

DOI=10.3389/fbioe.2022.927926

ISSN=2296-4185

ABSTRACT=<p><bold>Objective:</bold> To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm.</p><p><bold>Methods:</bold> 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians.</p><p><bold>Results:</bold> Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03).</p><p><bold>Conclusion:</bold> The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.</p>