Deep learning in fracture detection: a narrative review

Figures & data

Figure 1. Visualization of Artificial Intelligence sub-family.

Figure 2. Visualization of artificial neuron model. Where A₁–A_N are the inputs, W₁–W_N are the weights for the input connections to neuron, b is the bias value, z is the output from the neuron.

Figure 3. Deep learning aided workflow in fracture detection

Summary of clinical studies involving computer-aided fracture detection

Reference	Region of interest	Modality	Conclusion	Performance (metric)
Olczak et al. 2017	Wrist/Hand/Ankle	Radiographs	This study supports the use of orthopaedic radiographs of artificial intelligence, which can perform at a human level	0.83 (accuracy)
Kim et al. 2018	Wrist	Radiographs	The AUC scores for this test were comparable tostate-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs	0.95 (AUC) 0.90 (sensitivity) 0.88 (specificity)
Chung et al. 2018	Proximal humerus	Radiographs	The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs	Detection: 0.96 (accuracy) 1 (AUC) 0.99 (sensitivity) 0.97 (specificity) Classification: 0.65–0.86 (accuracy) 0.90–0.98 (AUC) 0.88–0.97 (sensitivity) 0.83–0.94 (specificity)
Heimer et al. 2018	Skull	CT	Classification based on the existence of skull fractures on CMIPs with deep learning is feasible	0.97 (AUC) 0.91 (sensitivity) 0.88 (specificity)
Lindsey et al. 2018	Wrist	Radiographs	Deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care	0.97 (AUC) on Test set1 0.98 (AUC) on Test set2
Tomita et al. 2018	Pelvis	CT	The proposed system will assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists	0.89 (accuracy) 0.91 (F1 score)
Pranata et al. 2019	Calcaneus	CT	The feasibility using deep CNN and SURF for computer-aided classification and detection of the location of calcaneus fractures in CT images	0.98 (accuracy)
Adams et al. 2019	Pelvis	Radiographs	As impressive as recognising fractures is for a DCNN, similar learning can be achieved by top-performing medically naпve humans with less than 1 hour of perceptual training	0.91 (accuracy) 0.98 (AUC)

Abbreviations: CT = computed tomography; AUC = area under curve; CNN = convolutional neural network; AP = plain anteroposterior;

CMIP = curved maximum intensity projections; OVF = Osteoporotic vertebral fractures; SURF = speeded-up robust features;

DCNN = deep convolutional neural networks.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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