Takaaki Urakawa1,2, Yuki Tanaka3, Shinichi Goto3, Hitoshi Matsuzawa4, Kei Watanabe5, Naoto Endo5. 1. Department of Orthopedic Surgery, Tsuruoka Municipal Shonai Hospital, 4-20 Izumi-machi, Tsuruoka-shi, Yamagata, 997-8515, Japan. takaaki-u@mwe.biglobe.ne.jp. 2. Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-Dori, Niigata, 951-8510, Japan. takaaki-u@mwe.biglobe.ne.jp. 3. Department of Orthopedic Surgery, Tsuruoka Municipal Shonai Hospital, 4-20 Izumi-machi, Tsuruoka-shi, Yamagata, 997-8515, Japan. 4. Center for Integrated Human Brain Science, Brain Research Institute, University of Niigata, 1-757 Asahimachi, Niigata, 951-8585, Japan. 5. Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-Dori, Niigata, 951-8510, Japan.
Abstract
OBJECTIVE: To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons. MATERIALS AND METHODS: In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons. RESULTS: The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1-97.6) and 92.2% (95% CI = 89.2-94.9), sensitivities of 93.9% (95% CI = 90.1-97.1) and 88.3% (95% CI = 83.3-92.8), and specificities of 97.4% (95% CI = 94.5-99.4) and 96.8% (95% CI = 95.1-98.4), respectively. CONCLUSIONS: The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.
OBJECTIVE: To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons. MATERIALS AND METHODS: In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons. RESULTS: The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1-97.6) and 92.2% (95% CI = 89.2-94.9), sensitivities of 93.9% (95% CI = 90.1-97.1) and 88.3% (95% CI = 83.3-92.8), and specificities of 97.4% (95% CI = 94.5-99.4) and 96.8% (95% CI = 95.1-98.4), respectively. CONCLUSIONS: The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.
Entities:
Keywords:
Convolutional neural network; Deep learning; Fracture; Orthopedics
Authors: Justin D Krogue; Kaiyang V Cheng; Kevin M Hwang; Paul Toogood; Eric G Meinberg; Erik J Geiger; Musa Zaid; Kevin C McGill; Rina Patel; Jae Ho Sohn; Alexandra Wright; Bryan F Darger; Kevin A Padrez; Eugene Ozhinsky; Sharmila Majumdar; Valentina Pedoia Journal: Radiol Artif Intell Date: 2020-03-25