Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments

Gan, Kaifeng; Xu, Dingli; Lin, Yimu; Shen, Yandong; Zhang, Ting; Hu, Keqi; Zhou, Ke; Bi, Mingguang; Pan, Langxing; Wu, Wei; Liu, Yunpeng

doi:10.1080/17453674.2019.1600125

Cited by 125 publications

(90 citation statements)

References 20 publications

(27 reference statements)

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“…They have successfully been used for fracture detection and localization on radiographs [3][4][5][6][7][8][9][10][11][12]. Training data for automated fracture detection have been heterogeneously labeled by orthopedic surgeons [5], orthopedic specialists [6], radiology [10,11,13,14] or orthopedic [15] residents and general radiologists [4] or specialized musculoskeletal radiologists [7,8]. Cheng et al [8] used registry data to label hip fractures on radiographs and only Olczak et al [12] used key phrases of radiology reports to label radiographs for the training set.…”

Section: Introductionmentioning

confidence: 99%

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size

et al. 2021

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Objectives To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm’s performance. Methods A total of 15,775 frontal and lateral radiographs, corresponding radiology reports, and a ResNet18 DCNN were used. Fracture detection and classification models were developed per view and merged. Incrementally sized subsets served to evaluate effects of the training set size. Two musculoskeletal radiologists set the standard of reference on radiographs (test set A). A subset (B) was rated by three radiology residents. For a per-study-based comparison with the radiology residents, the results of the best models were merged. Statistics used were ROC and AUC, Youden’s J statistic (J), and Spearman’s correlation coefficient (ρ). Results The models’ AUC/J on (A) for metal and cast were 0.99/0.98 and 1.0/1.0. The models’ and residents’ AUC/J on (B) were similar on fracture (0.98/0.91; 0.98/0.92) and multiple fragments (0.85/0.58; 0.91/0.70). Training set size and AUC correlated on metal (ρ = 0.740), cast (ρ = 0.722), fracture (frontal ρ = 0.947, lateral ρ = 0.946), multiple fragments (frontal ρ = 0.856), and fragment displacement (frontal ρ = 0.595). Conclusions The models trained on a DCNN with report-based labels to detect distal radius fractures on radiographs are suitable to aid as a secondary reading tool; models for fracture classification are not ready for clinical use. Bigger training sets lead to better models in all categories except joint affection. Key Points • Detection of metal and cast on radiographs is excellent using AI and labels extracted from radiology reports. • Automatic detection of distal radius fractures on radiographs is feasible and the performance approximates radiology residents. • Automatic classification of the type of distal radius fracture varies in accuracy and is inferior for joint involvement and fragment displacement.

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Section: Introductionmentioning

confidence: 99%

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size

et al. 2021

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“…However, studies using CNNs in the field of orthopedic surgery and traumatology are limited and the field is immature. So far, there are radiographic studies using CNNs for hip fractures (Adams et al 2019, Badgeley et al 2019, Cheng et al 2019, Urakawa et al 2019, distal radius fractures (Kim and MacKinnon 2018, Gan et al 2019, Yahalomi et al 2019, Blüthgen et al 2020, proximal humeral fractures (Chung et al 2018), ankle fractures (Kitamura et al 2019) and hand, wrist, and ankle fractures (Olczak et al 2017).…”

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Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs

et al. 2020

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Background and purpose — Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods — 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. 150 images each of the AP and lateral views were separated out and the remainder of the dataset was used for training. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score. We further compared the CNN’s performance with that of orthopedic surgeons. Results — The average accuracy, recall, precision, and F1 score of the CNN based on both anteroposterior and lateral radiographs were 0.98, 0.98, 0.98, and 0.98, respectively. The accuracy of the CNN was comparable to, or statistically significantly better than, that of the orthopedic surgeons regardless of radiographic view used. In the CNN model, the accuracy of the diagnosis based on both views was significantly better than the lateral view alone and tended to be better than the AP view alone. Interpretation — The CNN exhibited comparable or superior performance to that of orthopedic surgeons to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using both AP and lateral hip radiographs.

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“…In medicine, deep learning has notably been explored in specialties such as endocrinology for retinal photography [ 9 ], dermatology for recognizing cancerous lesions [ 10 ] and oncology for recognizing pulmonary nodules [ 11 ], as well as mammographic tumors [ 12 ]. In trauma orthopedics, the last four years have yielded several studies on deep learning for fracture recognition with very promising results [ 4 , 13 – 15 ], yet its applications and limitations are still largely unexplored [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

et al. 2021

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Background Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system. Methods We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002–2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session. Results We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance. Conclusion Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint.

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Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments

Cited by 125 publications

References 20 publications

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size

Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs

Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

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