Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures

Adams, Matthew; Chen, Weijia; Holcdorf, David; McCusker, Mark W; Howe, Piers D. L.; Gaillard, Frank

doi:10.1111/1754-9485.12828

Cited by 120 publications

(84 citation statements)

References 20 publications

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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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confidence: 99%

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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confidence: 99%

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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“…Our CAD system, based on a deep learning algorithm, had some advantages over other symptoms. We conducted a literature review that demonstrated the application of AI-based systems for the diagnosis of hip fracture in Table 4 [17][18][19]. We used the largest amount of learning data from multiple institutions.…”

Section: Discussionmentioning

confidence: 99%

“…However, they did not provide comparisons with health-care professionals (i.e., human vs. machine), and few of the studies reported comparisons with healthcare professionals using the same test dataset. As shown in Table 4, in previous studies on deep learning algorithms for hip fractures, no assessment was made as to how deep learning algorithms affect clinicians' diagnostic abilities [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

“…Olczak rst demonstrated that arti cial intelligence (AI) with the use of a deep learning approach for the diagnosis of ankle and wrist fracture on plain X-rays [16]. There have been some papers on the use of a deep learning algorithm to diagnose hip fractures [17][18][19]. Besides, a previous study reported a that deep learning algorithm improved the diagnostic accuracy of fracture detection by clinicians [20].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Improves the Accuracy of Residents in the Diagnosis of Hip Fractures –A Multicenter Study

Sato

Takegami

ASAMOTO³

et al. 2020

Preprint

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BackgroundLess experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system.MethodsA deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4,851 cases. These images were divided into 5242 images that included the fracture site and 5242 images that did not. Thus, a total of 10484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents and four orthopedic surgery fellows were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images.ResultsThe accuracy, sensitivity, specificity, F-value, and AUC were 96.1%, 95.2%, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system.ConclusionsWe developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures.Level of EvidenceLevel Ⅲ, Foundational evidence, before-after study. Clinical relevance: high

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“…32 Detection of Fractures on Radiographs and CT Multiple deep-learning methods have been used to detect fractures on radiographs. Most studies have used open-source CNNs and large training datasets for detecting fractures in multiple body parts including the hip, [33][34][35][36] shoulder, 36,37 wrist, 36,[38][39][40] and ankle 36,41 using the interpretation of experienced radiologists as the reference standard. Diagnostic performance varied but was generally high for all studies, with AUCs ranging between 0.90 and 0.99, sensitivities ranging between 73% and 99%, specificities ranging between 73% and 97%, and accuracies ranging between 75% and 96% ( Table 2).…”

Section: Estimation Of Pediatric Bone Age On Radiographsmentioning

confidence: 99%

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

Kijowski

Liu

Calivá

et al. 2019

Magnetic Resonance Imaging

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Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2

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Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures

Cited by 120 publications

References 20 publications

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

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 Improves the Accuracy of Residents in the Diagnosis of Hip Fractures –A Multicenter Study

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

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