Artificial intelligence as the clinical assistant for detection of femoral neck fracture: Intelligent medicine brings the bright future

Objective: The high rates of missed diagnosis and misdiagnosis limit the diagnosis of femoral neck fracture (FNF), which requires a new method to assist doctors to get more accurate diagnosis of FNF. This study aims to estimate the ability of AI in the detection of FNF and further compare its performance with human level. And the performance of AI-aided human level is also explored to confirm the value of AI as an assistant for clinical doctors to detect the FNF. Materials and methods: 4477 hip X-rays (consisted of 2884 FNF X-rays and 1593 normal hip X-rays) from eight Chinese top tree hospitals (Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (Wuhan Union Hospital), Wuhan Pu'ai Hospital, Tianyou Hospital, Wuhan University of Science and Technology, Hanyang Hospital, Wuhan University of Science and Technology, Northern Jiangsu People's Hospital, Xiangya Changde Hospital, People's Hospital of Tibet Autonomous Region and the Second Affiliated Hospital of Soochow University) were collected to establish a large multi-center clinical sample database. Then the X-rays were labeled, and the database was divided into training dataset (4029 X-rays) and testing dataset (448 X-rays). A Faster RCNN model with three different backbones (VGG16, VGG16-nottop and Resnet 50) was set up and trained with the training dataset, then the diagnostic performance of the Faster RCNN was assessed by the testing dataset and further compared with five doctors, in the form of accuracy, sensitivity, specificity, missed diagnosis rate, misdiagnosis rate, positive predictive value (PPV), negative predictive value (NPV), and time consumption. The result of the backbone with best performance was further set as reference for the doctor to diagnose the testing dataset again to confirm the value of AI as an assistant to detect the FNF. Results: Faster RCNN with Resnet 50 performed best compared with the other two backbones (VGG16 performed lowest, VGG16-nottop performed at medium level) in accuracy (0.82 vs 0.58 and 0.76), sensitivity (0.93 vs 0.83 and 0.94), specificity (0.62 vs 0.12 and 0.43), missed diagnosis rate (0.07 vs 0.17 and 0.06), misdiagnosis rate (0.38 vs 0.88 and 0.57), PPV (0.82 vs 0.63 and 0.75), NPV (0.82 vs 0.28 and 0.81) and time consumption (0.02h vs 0.04 h and 0.03h). And compared with human level, the Faster RCNN with Resnet 50 expressed better ability in terms of accuracy, sensitivity, missed diagnosis rate, NPV and time consumption, and worse ability in specificity and misdiagnosis rate. As for the PPV, there was not significant difference. Under the assistance of Faster RCNN with Resnet 50, the human level was enhanced in all aspects. Conclusion: As a new application of intelligent medicine, AI can be qualified in the detection of FNF, and can also be an excellent assistant for clinical doctors to improve the diagnosis of FNF.