Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era - Liu, Pengran; Lu, Lin; Chen, Yufei; Huo, Tongtong; Xue, Mingdi; Wang, Honglin; Fang, Ying; Xie, Yi; Xie, Mao; Ye, Zhewei (Frontiers in Bioengineering and Biotechnology, 2022)
Objective: 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 AI algorithm. Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for training database and 57 for test database. An Faster-RCNN algorithm was applied to be trained and detect the FIF on the X-rays. The performance of AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate and time consumption were calculated and compared with orthopedic attending physicians. Results: 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 ± 1.92min). About the sensitivity and missed diagnosis rate, there were no statistically difference between the AI and orthopedic attending physicians (0.89 vs 0.87 ± 0.03 and 0.11 vs 0.13 ± 0.03). Conclusion: The AI diagnostic algorithm was an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.