11,493
Views
65
CrossRef citations to date
0
Altmetric
Articles

Deep learning in fracture detection: a narrative review

, , , , , , , , & show all

  • Adams M, Chen W, Holcdorf D, McCusker M W, Howe P D, Gaillard F. Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol 2019; 63: 27–32.
  • Brett A, Miller C G, Hayes C W, Krasnow J, Ozanian T, Abrams K, Block J E, van Kuijk C. Development of a clinical workflow tool to enhance the detection of vertebral fractures: accuracy and precision evaluation. Spine 2009; 34: 2437–43.
  • Brink J A, Arenson R L, Grist T M, Lewin J S, Enzmann D. Bits and bytes: the future of radiology lies in informatics and information technology. Eur Radiol 2017; 27: 3647–3651.
  • Cha K H, Hadjiiski L, Samala R K, Chan H P, Caoili E M, Cohan R H. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 2016; 43: 1882.
  • Chen C, Seff A, Kornhauser A, Xiao J. Deepdriving: learning affordance for direct perception in autonomous driving. Conference: IEEE International Conference on Computer Vision (ICCV); 2015.
  • Chen H, Zhang Y, Kalra M K, Lin F, Chen Y, Liao P, Zhou J, Wang G. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging 2017; 36: 2524–35.
  • Chollet F. Xception: deep learning with depthwise separable convolutions. PDF, arxiv.org [cs.CV]; 2016.
  • Chung S W, Han S S, Lee J W, Oh K S, Kim N R, Yoon J P, Kim J Y, Moon S H, Kwon J, Lee H J, Noh Y M, Kim Y. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 2018; 89: 468–73.
  • Cireşan D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural Netw 2012; 32: 333–8.
  • Couteaux V, Si-Mohamed S, Nempont O, Lefevre T, Popoff A, Pizaine G, Villain N, Bloch I, Cotten A, Boussel L. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging 2019; 100: 235–42.
  • Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng P A. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 2017; 41: 40–54.
  • Esteva A, Kuprel B, Novoa R A, Ko J, Swetter S M, Blau H M, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115–118.
  • Faculty of Clinical Radiology, Clinical Radiology UK workforce census 2016 report; 2016. Available at: http://www.rcr.ac.uk.
  • Gao X W, Hui R, Tian Z. Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed 2017; 138: 49–56.
  • Gulshan V, Peng L, Coram M, Stumpe M C, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson P C, Mega J L, Webster D R. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402–10.
  • Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69S: S36–S40.
  • He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arxiv.org [cs.CV]; 2016.
  • He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. arxiv.org [cs.CV]; 2017.
  • Heimer J, Thali M J, Ebert L. Classification based on the presence of skull fractures on curved maximum intensity skull projections by means of deep learning. J Forensic Radiol Imaging 2018; 14: 16–20.
  • Hosny A, Parmar C, Quackenbush J Schwartz L H, Aerts H J W L. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18: 500–10.
  • Jha S, Topol E J. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 2016; 316: 2353–4.
  • Kahn C E. Artificial intelligence in radiology: decision support systems. Radiographics 1994; 14: 849–61.
  • Kim D H, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol 2018; 73: 439–45.
  • Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017; 284: 574–82.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436–44.
  • Lee J G, Jun S, Cho Y W, Lee H, Kim G B, Seo J B, Kim N. Deep learning in medical imaging: general overview. Korean J Radiol 2017; 18: 570–84.
  • Li X, Chen H, Qi X, Dou Q, Fu C W, Heng P A. H-Dense UNet: hybrid densely connected UNet for liver and tumor segmentation from ct volumes. IEEE Trans Med Imaging 2018; 37: 2663–74.
  • Li R, Zeng X, Sigmund S E, Lin R, Zhou B, Liu C, Wang K, Jiang R, Freyberg Z, Lv H, Xu M. Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN. BMC Bioinformatics 2019; 20: 132.
  • Lian S, Li L, Lian G, Xiao X, Luo Z, Li S. A global and local enhanced residual U-Net for accurate retinal vessel segmentation. IEEE/ACM Trans Comput Biol Bioinform 2019. [Epub ahead of print]
  • Liew C. The future of radiology augmented with artificial intelligence: a strategy for success. Eur J Radiol 2018; 102: 152–6.
  • Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, Hanel D, Gardner M, Gupta A, Hotchkiss R, Potter H. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci USA 2018; 115: 11591–6.
  • McCulloch W S, Pitts W H. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 1943; 5:115–33.
  • Mnih V, Kavukcuoglu K, Silver D, Rusu A A, Veness J, Bellemare M G, Graves A, Riedmiller M, Fidjeland A K, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D. Human-level control through deep reinforcement learning. Nature 2015; 518: 529–33.
  • Moravčík M, Schmid M, Burch N, Lisý V, Morrill D, Bard N, Davis T, Waugh K, Johanson M, Bowling M. DeepStack: expert-level artificial intelligence in heads-up no-limit poker. Science 2017; 356: 508–13.
  • National Institute for Health and Environment (Rijksinstituut voor volksgezondheid en milieu [RIVM]); 2016. Available at: https://www.rivm.nl/medische-stralingstoepassingen/trends-en-stand-van-zaken/diagnostiek/computer-tomografie/trends-in-aantal-ct-onderzoeken.
  • Olczak J, Fahlberg N, Maki A, Razavian A S, Jilert A, Stark A, Sköldenberg O, Gordon M. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 2017; 88: 581–6.
  • Pranata Y D, Wang K C, Wang J C, Idram I, Lai J Y, Liu J W, Hsieh I H. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed 2019; 171: 27–37.
  • Recht M, Bryan R N. Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol 2017; 14: 1476–80.
  • Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. arxiv.org [cs.CV]; 2015.
  • Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science 2015; 234–41. Available from http://dx.doi.org/10.1007/978-3-319-24574-428.
  • Roth H R, Oda H, Zhou X, Shimizu N, Yang Y, Hayashi Y, Oda M, Fujiwara M, Misawa K, Mori K. An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 2018; 66: 90–9.
  • Ruhan S, Owens W, Wiegand R, Studin M, Capoferri D, Barooha K, Greaux A, Rattray R, Hutton A, Cintineo J, Chaudhary V. Intervertebral disc detection in X-ray images using faster R-CNN. Conf Proc IEEE Eng Med Biol Soc 2017; 564–7.
  • Russakovsky O, Deng J, Su H. ImageNet Large Scale Visual Recognition Challenge. IJCV paper | bibtex | paper content on arxiv | attribute annotations; 2015.
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Published as a conference paper at ICLR; 2015.
  • Szegedy C, Vanhoucke V, Loffe S. Rethinking the Inception Architecture for Computer Vision. arxiv.org [cs.CV]; 2015.
  • Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, Chepelev L, Cairns R, Mitchell J R, Cicero M D, Poudrette M G, Jaremko J L, Reinhold C, Gallix B, Gray B, Geis R; Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018; 69: 120–35.
  • Ting D S W, Cheung C Y, Lim G, Tan G S W, Quang N D, Gan A, Hamzah H, Garcia-Franco R, San Yeo I Y, Lee S Y, Wong E Y M, Sabanayagam C, Baskaran M, Ibrahim F, Tan N C, Finkelstein E A, Lamoureux E L, Wong I Y, Bressler N M, Sivaprasad S, Varma R, Jonas J B, He M G, Cheng C Y, Cheung G C M, Aung T, Hsu W, Lee M L, Wong T Y. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318: 2211–23.
  • 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.
  • Tompson J, Jain A, LeCun Y, Bregler C. Joint training of a convolutional network and a graphical model for human pose estimation. Advances in Neural Information Processing Systems 2014; 27: 1799–1807.
  • Tran G S, Nghiem T P, Nguyen V T, Luong C M, Burie J C. Improving accuracy of lung nodule classification using deep learning with focal loss. J Healthc Eng 2019; 5156416.
  • 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 2019; 48: 239–44.
  • Wolterink J M, Leiner T, Viergever M A, Isgum I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 2017; 36: 2536–45.
  • Yang Y, Yan L F, Zhang X, Han Y, Nan H Y, Hu Y C, Hu B, Yan S L, Zhang J, Cheng D L, Ge X W, Cui G B, Zhao D, Wang W. Glioma grading on conventional MR images: a deep learning study with transfer learning. Front Neurosci 2018; 12: 804.
  • Zhu H, Shi F, Wang L, Hung S C, Chen M H, Wang S, Lin W, Shen D. Dilated dense U-Net for infant hippocampus subfield segmentation. Front Neuroinform 2019; 13: 30.