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Deep neural network improves fracture detection by clinicians

Robert Lindsey, Aaron Daluiski, Sumit Chopra, Alexander Lachapelle, Michael Mozer, Serge Sicular, Douglas Hanel, Michael Gardner, Anurag Gupta, Robert Hotchkiss, Hollis Potter
2018 Proceedings of the National Academy of Sciences of the United States of America  
In this work, we developed a deep neural network to detect and localize fractures in radiographs.  ...  We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model.  ...  The project is funded by Imagen Technologies. The work presented in this manuscript is for research purposes only and is not for sale within the United States.  ... 
doi:10.1073/pnas.1806905115 fatcat:dwfxp675wfcubboachblsqzwfm

Practical computer vision application to detect hip fractures on pelvic X-rays: a bi-institutional study

Jeff Choi, James Z Hui, David Spain, Yi-Siang Su, Chi-Tung Cheng, Chien-Hung Liao
2021 Trauma Surgery & Acute Care Open  
Xception was our convolutional neural network structure.  ...  We hypothesized a convolutional neural network algorithm can accurately diagnose hip fractures on PXR and a web application can facilitate its bedside adoption.MethodsThe development cohort comprised 4235  ...  Computer vision algorithm Our convolutional neural network structure was Xception. 9 Convolutional neural network is a deep learning methodology that permits image pattern recognition based on filters  ... 
doi:10.1136/tsaco-2021-000705 pmid:33912689 pmcid:PMC8031685 fatcat:kzpt5t4uxjemrcj6nzbxeif4gi

FRACTURE DETECTION: A QUICK SURVEY OF DEEP LEARNING MODELS

Irfan Khatik, Nilesh Mahajan
2021 Zenodo  
This paper focuses on deep learning methods for bone fracture detection. Deep learning is a Neural Network based method where more hidden layers are used with the artificial neural network.  ...  Missing a fracture has severe consequences on patients. Automated detection of bone fracture is a hot research topic today. There are a lot of papers on automated fracture detection.  ...  “Deep neural network improves fracture detection by clinicians”,PNAS November 6, 2018 115 (45) 11591- 11596; first published October 22, 2018; [13] D. P. Yadav and S.  ... 
doi:10.5281/zenodo.5763104 fatcat:rwvpvk7b6bffzfixkjpaonkp4e

An Algorithm for Automatic Rib Fracture Recognition Combined with nnU-Net and DenseNet

Junzhong Zhang, Zhiwei Li, Shixing Yan, Hui Cao, Jing Liu, Dejian Wei, Zhaohui Liang
2022 Evidence-Based Complementary and Alternative Medicine  
The results show that the two-stage deep learning model proposed in this study improves the accuracy of rib fracture recognition and reduces the false-positive and false-negative rates of rib fracture  ...  detection, which can better assist doctors in fracture region recognition.  ...  Acknowledgments is research was sponsored by the National Natural Science Foundation of China (nos. 82074579 and 81973981), the Traditional Chinese Medicine Science and Technology Project of Shandong Province  ... 
doi:10.1155/2022/5841451 pmid:35251210 pmcid:PMC8896936 fatcat:gz6sk2k6qvcmhmvx637tmpqe2e

Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection

Ashutosh Jadhav, Ken C L Wong, Joy T Wu, Mehdi Moradi, Tanveer Syeda-Mahmood
2021 AMIA Annual Symposium Proceedings  
The annotated X-rays are used to train a deep neural network classifier for finding detection.  ...  Finally, we developed a knowledge-driven reasoning algorithm that leverages knowledge learned from X-ray reports to improve upon the deep learning module's performance on finding detection.  ...  The annotated X-rays are then used to train a multi-label deep neural network classifier for CXR finding detection.  ... 
pmid:33936433 pmcid:PMC8075485 fatcat:vlnraayghvegjli2kghaxrtpvq

Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs

Hsuan-Yu Chen, Benny Wei-Yun Hsu, Yu-Kai Yin, Feng-Huei Lin, Tsung-Han Yang, Rong-Sen Yang, Chih-Kuo Lee, Vincent S. Tseng, Yan Chai Hum
2021 PLoS ONE  
This study uses a deep convolutional neural network (DCNN) to identify the feasibility for the screening, detection, and localization of VFs using PARs.  ...  The proposed model can help clinicians become more efficient and economical in the current clinical pathway of fragile fracture treatment.  ...  Many methods such as gradient-weighted class activation mapping (Grad-CAM) [8] have been developed for the visual depiction of a deep convolutional neural network (DCNN) to assist clinicians in identifying  ... 
doi:10.1371/journal.pone.0245992 pmid:33507982 fatcat:jrzlcdzd2ze57gfxkegbfz2uui

Determining the Location of Tibial Fracture of Dog and Cat Using Hybridized Mask R-CNN Architecture

Berker BAYDAN, Necaattin BARIŞÇI, Halil Murat ÜNVER
2021 Kafkas Universitesi Veteriner Fakultesi Dergisi  
The aim of this study is to hybridize the original backbone structure used in the Mask R-CNN framework, and to detect fracture location in dog and cat tibia fractures faster and with higher performance  ...  With the hybrid study, it will be ensured that veterinarians help diagnose fractures on the tibia with higher accuracy by using a computerized system.  ...  Convolutional neural network (CNN) is a class of deep neural networks where in deep learning most commonly used to analyze images and video processing [6] .  ... 
doi:10.9775/kvfd.2021.25486 doaj:e1e60287f6794430a3fb094af09fa00c fatcat:nyyc6bkj7jef3pzsymlo4whheq

Application of convolutional neural networks for distal radio-ulnar fracture detection on plain radiographs in the emergency room

Min Woong Kim, Jaewon Jung, Se Jin Park, Young Sun Park, Jeong Hyeon Yi, Won Seok Yang, Jin Hyuck Kim, Bum-Joo Cho, Sang Ook Ha
2021 Clinical and Experimental Emergency Medicine  
to detect wrist fractures.  ...  Objective Recent studies have suggested that deep-learning models can satisfactorily assist in fracture diagnosis.  ...  They reported that the ability of clinicians to diagnose wrist fractures could be improved with the aid of a deep-learning model.  ... 
doi:10.15441/ceem.20.091 fatcat:ovdztfkkrvgyvfvujf4zjmiym4

Assessing the Clinicians' Pathway to Embed Artificial Intelligence for Assisted Diagnostics of Fracture Detection

Carlos Francisco Moreno-García, Truong Dang, Kyle Martin, Manish Patel, Andrew Thompson, Lesley Leishman, Nirmalie Wiratunga
2020 European Conference on Artificial Intelligence  
the fracture detection task.  ...  Fracture detection has been a long-standing paradigm on the medical imaging community.  ...  [9] demonstrated that a deep neural network trained on 256'000 x-rays could detect fractures with a similar diagnostic accuracy to a sub-specialised orthopaedic surgeon. Also, Olczak et al.  ... 
dblp:conf/ecai/Moreno-GarciaDM20 fatcat:kuo6lon2ibcvrd6gxsimof6c2m

Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs

Guillermo Sánchez Rosenberg, Andrea Cina, Giuseppe Rosario Schiró, Pietro Domenico Giorgi, Boyko Gueorguiev, Mauro Alini, Peter Varga, Fabio Galbusera, Enrico Gallazzi
2022 Medicina  
Following augmentation, these single vertebra images were used to train, validate, and comparatively test two deep learning convolutional neural network models, namely ResNet18 and VGG16.  ...  The aim of this study was to develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine.  ...  Deep learning (DL) is a machine learning method that uses an algorithmic structure most commonly based on neural networks, such as convolutional neural networks.  ... 
doi:10.3390/medicina58080998 pmid:35893113 pmcid:PMC9330443 fatcat:636hpbqvubg4le3u34gv5c7kay

Fine-Tuned Deep Convolutional Networks for the Detection of Femoral Neck Fractures on Pelvic Radiographs: A Multicenter Dataset Validation

Lin Mu, Taiping Qu, Dong Dong, Xiuli LI, Yun Pei, Yuchong Wang, Guangyao Shi, Yongrui Li, Fujin He, Huimao Zhang
2021 IEEE Access  
In this study, we aim to provide a deep convolutional network based femoral neck fracture detection system on radiographs for emergency patients.  ...  The object detection model that is fine-tuned has high sensitivity and specificity and the universal ability to detect and locate femoral neck fractures on pelvic radiographs.  ...  ACKNOWLEDGMENT The authors are grateful to the Deepwise Corporation for their help with the deep learning algorithm used in this research. (Lin Mu and Taiping Qu contribute equally to this work.)  ... 
doi:10.1109/access.2021.3082952 fatcat:z336s57kmbb57c4l23yu6humri

Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures

Georg Zdolsek, Yupei Chen, Hans-Peter Bögl, Chunliang Wang, Mischa Woisetschläger, Jörg Schilcher
2021 Acta Orthopaedica  
We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs.Patients and methods - We entered  ...  We tested several deep neural network structures (i.e., VGG19, InceptionV3, and ResNet) to identify the network with the highest diagnostic accuracy for distinguishing AFF from NFF.  ...  We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs.  ... 
doi:10.1080/17453674.2021.1891512 pmid:33627045 pmcid:PMC8381921 fatcat:ydlew6tx55bfdohxb2qphcx7iu

How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

Guillaume Reichert, Ali Bellamine, Matthieu Fontaine, Beatrice Naipeanu, Adrien Altar, Elodie Mejean, Nicolas Javaud, Nathalie Siauve
2021 Journal of Imaging  
Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians.  ...  For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%).  ...  Deep learning (DL) is a subfield of machine learning relating to algorithms inspired by the structure and function of the brain, known as artificial neural networks.  ... 
doi:10.3390/jimaging7070105 fatcat:ywky34fl2bgmdnkxg3q2c5srya

Applications of Machine Learning in Bone and Mineral Research

Sung Hye Kong, Chan Soo Shin
2021 Endocrinology and Metabolism  
Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance.  ...  Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.  ...  learning"[Mesh] OR "Neural Networks, Computer" [Mesh] OR "artificial Intelligence"[tiab] OR "machine learning" [tiab] OR "deep learning"[tiab] OR "neural network*"[tiab]) AND English[la]).  ... 
doi:10.3803/enm.2021.1111 pmid:34674509 pmcid:PMC8566132 fatcat:qksyzdufqzd5tglmwmgwxjutsq

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Jeffrey Liu, Bino Varghese, Farzaneh Taravat, Liesl S. Eibschutz, Ali Gholamrezanezhad
2022 Diagnostics  
Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads  ...  While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide  ...  Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Sci Rep 11, 11716 (2021). https://doi.org/10.1038/s41598- 021-91144-z.  ... 
doi:10.3390/diagnostics12061351 pmid:35741161 pmcid:PMC9221728 fatcat:chuolsu2ufbfpnswaxca3mjonm
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