Abstract
In recent years, fracture image diagnosis using a convolutional neural network (CNN) has been reported. The purpose of the present study was to evaluate the ability of CNN to diagnose distal radius fractures (DRFs) using frontal and lateral wrist radiographs. We included 503 cases of DRF diagnosed by plain radiographs and 289 cases without fracture. We implemented the CNN model using Keras and Tensorflow. Frontal and lateral views of wrist radiographs were manually cropped and trained separately. Fine-tuning was performed using EfficientNets. The diagnostic ability of CNN was evaluated using 150 images with and without fractures from anteroposterior and lateral radiographs. The CNN model diagnosed DRF based on three views: frontal view, lateral view, and both frontal and lateral view. We determined the sensitivity, specificity, and accuracy of the CNN model, plotted a receiver operating characteristic (ROC) curve, and calculated the area under the ROC curve (AUC). We further compared performances between the CNN and three hand orthopedic surgeons. EfficientNet-B2 in the frontal view and EfficientNet-B4 in the lateral view showed highest accuracy on the validation dataset, and these models were used for combined views. The accuracy, sensitivity, and specificity of the CNN based on both anteroposterior and lateral radiographs were 99.3, 98.7, and 100, respectively. The accuracy of the CNN was equal to or better than that of three orthopedic surgeons. The AUC of the CNN on the combined views was 0.993. The CNN model exhibited high accuracy in the diagnosis of distal radius fracture with a plain radiograph.
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References
Nellans KW, Kowalski E, Chung KC: The epidemiology of distal radius fractures. Hand Clin 28:113–125, 2012
Mauffrey C, Stacey S, York P J, Ziran B H, Archdeacon M T: Radiographic evaluation of acetabular fractures: review and update on methodology. J Am Acad Orthop Surg 26(3); 83–93, 2018
Waever D, Madsen M L, Rolfing J H D, Borris L C, Henriksen M, Nagel L, Thorninger R: Distal radius fractures are difficult to classify. Injury 49 (Suppl .1); S29–S32, 2018
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542; 115–118, 2017
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL Webster DR: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316; 2402–10, 2016
Lakhani P, Sundaram B: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284; 574–82, 2017
Adams M, Gaillard F, Chen W, Holcdorf D, Mccusker MW, Howe PDL, Gillard F: Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol 63(1); 27–32, 2018
Badgeley MA, Zech JR, Oakden-rayner L, Glicksberg BS, Liu M, Gale W, McConnell M V, Percha B, Snyder TM, Dudley JT: Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit Med 2(1); 1–10, 2019
Cheng C, Ho T, Lee T, Chang C, Chou C, Chen C: Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol 29(10); 5469-5477, 2019
Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N: Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 48(2); 239–244, 2019
Kim DH, MacKinnon T: Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol 73(5); 439–45, 2018
Gan K, Xu D, Lin Y, Shen Y, Zhang T, Hu K, Zhou K, Bi M, Pan L, Wu W, Liu Y: Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop 90(4); 394–400, 2019
Yahalomi E, Chernofsky M, Werman M: Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN. Adv Intell Syst Comput 997; 971–81, 2019
Blüthgen C, Becker AS, Martini IV De, Meier A, Martini K, Frauenfelder T: Detection and localization of distal radius fractures: deep learning system versus radiologists. Eur J Radiol 126; 108925, 2020
Chung SW, Han SS, Lee JW, Oh K, Kim NR, Yoon JP, Kim JY, Moon SH, Kwon J, Lee H, Noh Y, Kim Y, Chung SW, Han SS, Lee JW, Oh K, Kim NR, Yoon JP: Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 89(4); 468–473, 2018
Kitamura G, Chung CY, Moore BE: Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. J Digit Imaging 32(4); 672–677, 2019
Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, Sköldenberg O, Gordon M: Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop 88(6); 581–586, 2017
Tan M, Le QV: Efficientnet: rethinking model scaling for convolutional neural networks. International Conference on Machine Learning [Internet]. 2019. Available from: arXiv:1905.11946. Accessed 18 June 2020
Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT: Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol Artif Intell 1(1); e180001, 2019
van der Plaats GJ: Medical X-ray technique, Eindhoven, The Netherlands: Macmillan International Higher Education, 1969
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D: Grad-cam: visual explanations from deep networks via gradient-based localization, in 2016 IEEE International Conference on Computer Vision; 618–626, 2016. arXiv:1610.02391. Accessed 18 June 2020
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Suzuki, T., Maki, S., Yamazaki, T. et al. Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy Comparable to Hand Orthopedic Surgeons. J Digit Imaging 35, 39–46 (2022). https://doi.org/10.1007/s10278-021-00519-1
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DOI: https://doi.org/10.1007/s10278-021-00519-1