1) Olczak J, Fahlberg N, Maki A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 2017;88(6):581-6.
2) Kim DH, MacKinnon T. Artificial intelligence in fracture detection:transfer learning from deep convolutional neural networks. Clin Radiol 2018;73(5):439-45.
3) Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 2018;89(4):468-473.
4) Urakawa T, Tanaka Y, Goto S, et al. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 2019;48(2):239-44.
5) Lindsey R, Daluiski A, Chopra S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A 2018;115(45):11591-6.
6) Badgeley MA, Zech JR, Oakden-Rayner L, et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit Med 2019;2:31.
7) Pranata YD, Wang KC, Wang JC, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed 2019;171:27-37.
8) Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 2018;98:8-15.
9) Hirano T, Nishida M, Nonaka N, et al. Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis. Rheumatol Adv Pract 2019;3(2):rkz047.
10) Kato K, Yasojima N, Tamura K, et al. Detection of fine radiographic progression in finger joint space narrowing beyond human eyes:phantom experiment and clinical study with rheumatoid arthritis patients. Sci Rep 2019;9(1):8526.