J S Yu1, S M Yu2, B S Erdal2, M Demirer2, V Gupta2, M Bigelow2, A Salvador2, T Rink2, S S Lenobel2, L M Prevedello2, R D White2. 1. Department of Radiology, The Ohio State University College of Medicine, 452 Doan Tower, 395 West 12th Avenue, Columbus, OH 43210, USA. Electronic address: Joseph.yu@osumc.edu. 2. Department of Radiology, The Ohio State University College of Medicine, 452 Doan Tower, 395 West 12th Avenue, Columbus, OH 43210, USA.
Abstract
AIM: To investigate the feasibility of applying a deep convolutional neural network (CNN) for detection/localisation of acute proximal femoral fractures (APFFs) on hip radiographs. MATERIALS AND METHODS: This study had institutional review board approval. Radiographs of 307 patients with APFFs and 310 normal patients were identified. A split ratio of 3/1/1 was used to create training, validation, and test datasets. To test the validity of the proposed model, a 20-fold cross-validation was performed. The anonymised images from the test cohort were shown to two groups of radiologists: musculoskeletal radiologists and diagnostic radiology residents. Each reader was asked to assess if there was a fracture and localise it if one was detected. The area under the receiver operator characteristics curve (AUC), sensitivity, and specificity were calculated for the CNN and readers. RESULTS: The mean AUC was 0.9944 with a standard deviation of 0.0036. Mean sensitivity and specificity for fracture detection was 97.1% (81.5/84) and 96.7% (118/122), respectively. There was good concordance with saliency maps for lesion identification, but sensitivity was lower for characterising location (subcapital/transcervical, 84.1%; basicervical/intertrochanteric, 77%; subtrochanteric, 20%). Musculoskeletal radiologists showed a sensitivity and specificity for fracture detection of 100% and 100% respectively, while residents showed 100% and 96.8%, respectively. For fracture localisation, the performance decreased slightly for human readers. CONCLUSION: The proposed CNN algorithm showed high accuracy for detection of APFFs, but the performance was lower for fracture localisation. Overall performance of the CNN was lower than that of radiologists, especially in localizing fracture location.
AIM: To investigate the feasibility of applying a deep convolutional neural network (CNN) for detection/localisation of acute proximal femoral fractures (APFFs) on hip radiographs. MATERIALS AND METHODS: This study had institutional review board approval. Radiographs of 307 patients with APFFs and 310 normal patients were identified. A split ratio of 3/1/1 was used to create training, validation, and test datasets. To test the validity of the proposed model, a 20-fold cross-validation was performed. The anonymised images from the test cohort were shown to two groups of radiologists: musculoskeletal radiologists and diagnostic radiology residents. Each reader was asked to assess if there was a fracture and localise it if one was detected. The area under the receiver operator characteristics curve (AUC), sensitivity, and specificity were calculated for the CNN and readers. RESULTS: The mean AUC was 0.9944 with a standard deviation of 0.0036. Mean sensitivity and specificity for fracture detection was 97.1% (81.5/84) and 96.7% (118/122), respectively. There was good concordance with saliency maps for lesion identification, but sensitivity was lower for characterising location (subcapital/transcervical, 84.1%; basicervical/intertrochanteric, 77%; subtrochanteric, 20%). Musculoskeletal radiologists showed a sensitivity and specificity for fracture detection of 100% and 100% respectively, while residents showed 100% and 96.8%, respectively. For fracture localisation, the performance decreased slightly for human readers. CONCLUSION: The proposed CNN algorithm showed high accuracy for detection of APFFs, but the performance was lower for fracture localisation. Overall performance of the CNN was lower than that of radiologists, especially in localizing fracture location.
Authors: Rachel Y L Kuo; Conrad Harrison; Terry-Ann Curran; Benjamin Jones; Alexander Freethy; David Cussons; Max Stewart; Gary S Collins; Dominic Furniss Journal: Radiology Date: 2022-03-29 Impact factor: 29.146