Convolutional Neural Networks for Automated Fracture Detection and                     Localization on Wrist Radiographs

Thian, Yee Liang; Yi-ting, LI; Jagmohan, Pooja; Sia, David Soon Yiew; Chan, Vincent Ern Yao; Tan, Robby T.

doi:10.1148/ryai.2019180001

Cited by 126 publications

(96 citation statements)

References 14 publications

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“…Again, the combined performance was on par with dermatologists and outperformed non-dermatologists. R-CNNs have been used in fracture detection in radiology [55] and the detection of the nail plate in onychomycosis [56]. Algorithms could potentially detect and diagnosis skin cancer without any preselection of suspicious lesions by dermatologists.…”

Section: Non-melanoma Skin Cancermentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

165

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Section: Non-melanoma Skin Cancermentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

165

108

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“…The research proposed a model based on Inception-ResNet and a Faster R-CNN architecture was accomplished as a final model. The proposed model was tested on a 524 radiographs of wrist [7]. Rajpurkar et al trained a 169-layer Dense as a base model to identify and localize bone disorders(abnormalities).…”

Section: Related Workmentioning

confidence: 99%

Musculoskeletal Abnormality Detection in Humerus Radiographs Using Deep Learning

Chawla¹,

Kapoor²

2020

RIA

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Musculoskeletal radiographs bring a considerable amount of meticulous expertise in treating Bone diseases (BDs) or injuries. Usually, less experienced doctors are the first ones for assessment of radiographs and it is not surprising for humerus disorders being misdiagnosed. To take care of such misdiagnosis, Deep Learning and Machine Learning could play a major role in diagnosis of the musculoskeletal abnormalities. The presented paper intends to develop a better performing Computer Based Diagnosis (CBDs) model. First, some preprocessing techniques are performed on the chosen dataset of humerus radiographs, eliminating image size variability from the radiographs. Next, two architectures namely-DenseNet201 and Inception V3 were used to classify the given dataset as abnormal or normal. Later, ensemble techniques are applied to improve model's performance. The proposed technique is tested for the publicly available Musculoskeletal Radiographs (MURA) dataset and the qualifier results are compared with present results from the reference paper. For humerus radiographs, the accuracy achieved is 88.54%. Implementation results show the proposed method is a deserving strategy to classify bone disorders.

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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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Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs

Cited by 126 publications

References 14 publications

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Musculoskeletal Abnormality Detection in Humerus Radiographs Using Deep Learning

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

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