Automatic analysis system of calcaneus radiograph: Rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation

Guo, Jia; Mu, Yuxuan; Deng, Xue; Li, Huiqi; Chen, Junxian; Huang, Yan; Xu, Hailin; Wang, Wei

doi:10.1016/j.cmpb.2021.106124

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…The ResNet network trained by Olczak et al can classify ankle fractures based on the AO/OTA system, with an AUC of 0.90 ( 36 ). In addition to these common lower extremity fractures, deep learning can also assist in diagnosing and identifying calcaneus fractures by accurately evaluating Bohler's angle (BA) and critical angle of Gissane (CAG) on X-rays ( 37 ).…”

Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

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Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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“…They firstly introduced a histogram equalization to boost the contrast and used pose-driven learning to learn the positions of the vertebrae, and then a level-set method is combined with M-Net to refine the segmentation results. Guo et al (2021) proposed a twostage architecture to segment calcaneus in x-ray. Firstly, they used the coarse-to-fine Rotation-Invariant Regression-Voting method to detect landmarks, and on the basis of which they exploited a U-Net with an assistant classification output to segment calcaneus, effectively utilizing relationships between classification and segmentation.…”

Section: Image Segmentationmentioning

confidence: 99%

A review of advances in image-guided orthopedic surgery

Fan¹,

Zhu²,

Tu³

et al. 2023

Phys. Med. Biol.

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Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in Artificial Intelligence (AI), Deep Learning (DL), Augmented Reality (AR) and Robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and deep learning based medical image segmentation, 3D reconstruction and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with augmented reality (AR) and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.

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“…In Calcaneus Radiograph Investigation [16], the presentation was presented to retrieve the characteristics using the SIFT descriptor and the subsequent classification was performed through CNN. This contribution indicated that robust variation of the overall approach may be the appropriate candidate for radiographic classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal deep learning network for musculoskeletal X-Ray imaging classification for hospital medical record system

الرشيدي¹,

الرشيدي²,

العضيبي³

2022

Journal of Engineering Sciences and Information Technology

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Background: Medical image archiving is one of the integral components of any hospital medical record system (HMRS). It includes, but is not limited to, MRI, CT-Scan, X-ray, Ultrasound, Musculoskeletal X-rays etc. The musculoskeletal X-ray images are relatively significant in number among the other types of medical imaging. Most of the existing HMRS use either the manual annotation of the images or use metadata for every image for archiving. This approach is found to be deficient because of intensive manual work, chances of misclassification, and reliance on human expertise. Moreover, archiving the images and their metafiles is relatively difficult to handle. Methodology: This issue can be handled by a hybrid solution of computer vision and deep learning. In the recent literature, researchers have proposed using machine learning and deep learning algorithms for biomedical image classification and archiving. However, the literature is found to be insufficient to recommend a unified deep learning network for Musculoskeletal X-ray Image classification with greater accuracy and efficiency. The LERA dataset is considered one of the benchmark Musculoskeletal X-rays image datasets. Results: To the best of knowledge the investigation of the best candidate of deep neural network is still missing in the literature. This study will present the logical and empirical rationale for the recommendation of the optimal deep learning network for X-ray Imaging Classification for Hospital Medical Record System using LERA (musculoskeletal radiographs) dataset. It has been concluded that the variants of Resnet, Google Net, and DarkNet are the suggested candidates for LERA x-ray image classification.

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Automatic analysis system of calcaneus radiograph: Rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation

Cited by 10 publications

References 26 publications

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

A review of advances in image-guided orthopedic surgery

Optimal deep learning network for musculoskeletal X-Ray imaging classification for hospital medical record system

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