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Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection [article]

Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki, Elina Järvenpää, Kaisa Makkonen, Pekka Pinola, Tuula Palsio, Arttu Niemensivu, Osmo Tervonen, Aleksei Tiulpin
2021 arXiv   pre-print
Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks.  ...  In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection -- DeepWrist, and evaluated it against one general population test set, and  ...  Discussion In this study, we followed the recent works and trained a CNN-based pipeline for distal radius wrist fracture detection.  ... 
arXiv:2012.02577v2 fatcat:6vy5krxf55cjdiev6gxomn7vai

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  
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.  ...  In this work, we developed a deep neural network to detect and localize fractures in radiographs.  ...  We thank Andrew Sama for helping to run the study, Thomas Kho for programming the software for the human experiment, and Sophia Paul for helping with reader recruitment.  ... 
doi:10.1073/pnas.1806905115 fatcat:dwfxp675wfcubboachblsqzwfm

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs

Rebecca M. Jones, Anuj Sharma, Robert Hotchkiss, John W. Sperling, Jackson Hamburger, Christian Ledig, Robert O'Toole, Michael Gardner, Srivas Venkatesh, Matthew M. Roberts, Romain Sauvestre, Max Shatkhin (+8 others)
2020 npj Digital Medicine  
Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system.  ...  Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability.  ...  GenEngine, LLC conducted and is responsible for the primary statistical analyses. We thank Mike Mozer for providing valuable advice.  ... 
doi:10.1038/s41746-020-00352-w pmid:33145440 pmcid:PMC7599208 fatcat:g4xpabai2rc43mhdlvr4rwjzbq

Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays [article]

Antoine Spahr, Behzad Bozorgtabar, Jean-Philippe Thiran
2021 arXiv   pre-print
Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very time-consuming to acquire.  ...  Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.  ...  However, most of these methods are domain-specific, and their efficacy for anomaly detection has not been explored yet.  ... 
arXiv:2102.09895v2 fatcat:nzf4mmcqyrcutafxr4jhtobalu

Transfer Learning for an Automated Detection System of Fractures in Patients with Maxillofacial Trauma

Maria Amodeo, Vincenzo Abbate, Pasquale Arpaia, Renato Cuocolo, Giovanni Dell'Aversana Orabona, Monica Murero, Marco Parvis, Roberto Prevete, Lorenzo Ugga
2021 Applied Sciences  
An original maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, is proposed to detect traumatic fractures in patients.  ...  An additional 30 CT scans, comprising 25 "fracture" and 5 "noFracture" images, were used as the test dataset for final testing.  ...  Data Availability Statement: The data presented in this study are not available due to privacy restrictions.  ... 
doi:10.3390/app11146293 fatcat:rsltvrwwtjc6fl7m2ieq6iczay

CNN-based radiographic acute tibial fracture detection in the setting of open growth plates [article]

Zbigniew A Starosolski, Herman Kan, Ananth V Annapragada
2018 bioRxiv   pre-print
Here, we assess the performance of a convolutional neural network for the detection of acute tibial fractures trained with a limited number of cases in skeletally immature patients.  ...  We used a modified transfer learning approach based on the Xception architecture with additional fully convoluted reasoning and drop-out layers.  ...  A very recent study [7] utilized transfer learning to detect adult wrist fractures in radiographs, but to our knowledge, no previous studies have successfully applied transfer learning to the problem  ... 
doi:10.1101/506154 fatcat:qyabw2r4unejhcekxbz375zrdu

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Chi-Tung Cheng, Yirui Wang, Huan-Wu Chen, Po-Meng Hsiao, Chun-Nan Yeh, Chi-Hsun Hsieh, Shun Miao, Jing Xiao, Chien-Hung Liao, Le Lu
2021 Nature Communications  
We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation.  ...  Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey.  ...  Acknowledgements The authors thank CMRPG3H0971, CMRPG3J631, NCRPG3J0012 (MOST-108-2622-B-182A-002), and CIRPG3H0021 for supporting the development of the system.  ... 
doi:10.1038/s41467-021-21311-3 pmid:33594071 fatcat:df46u2znovdq5m7hhk6dicffxi

Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network [article]

Alireza Borjali, Antonia F. Chen, Orhun K. Muratoglu, Mohammad A. Morid, Kartik M. Varadarajan
2019 arXiv   pre-print
In this study, we present a novel, fully automatic and interpretable approach to detect mechanical loosening of THR implants from plain radiographs using deep convolutional neural network (CNN).  ...  Currently, radiographs are assessed manually by medical professionals, which may be prone to poor inter and intra observer reliability and low accuracy.  ...  We still observed that the re-trained CNN did not learn clear features and still looked for somewhat blurry features of different colors, while the pre-trained CNN learned complex, yet clear and identifiable  ... 
arXiv:1912.00943v1 fatcat:5qfcmiyn65divbeagd5mwv374m

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Armando Collado-Villaverde, Mario Cobos, Pablo Muñoz, David F. Barrero
2020 Electronics  
These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models' training.  ...  Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications.  ...  The authors would like to thank Mike de Pumpo for proof reading this article. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics9111831 fatcat:rwgh2mtglbcnpkcr3tpl6oylii

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Jerry Chen, Maysam Abbod, Jiann-Shing Shieh
2021 Sensors  
Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life  ...  By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection.  ...  In practice, there are multiple scales and measures that are helpful for tracking painrelated treatment outcomes.  ... 
doi:10.3390/s21041030 pmid:33546235 fatcat:woeoeixoinftpcn3u4bov2rpla

Biomedical Applications of Electromagnetic Detection: A Brief Review

Pu Huang, Lijun Xu, Yuedong Xie
2021 Biosensors  
Finally, the future development to electromagnetic detection for biomedical applications are presented.  ...  In addition, electromagnetic detection in combination with machine learning (ML) technology has been used in clinical diagnosis because of its powerful feature extraction capabilities.  ...  Deep learning (DL) is a technology that realizes machine learning.  ... 
doi:10.3390/bios11070225 pmid:34356696 pmcid:PMC8301974 fatcat:trvxnwyajjdmvluynvg5xspmwa

Possible Life Saver: A Review on Human Fall Detection Technology

Zhuo Wang, Vignesh Ramamoorthy, Udi Gal, Allon Guez
2020 Robotics  
Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population.  ...  Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized.  ...  Acknowledgments : The authors wish to thank the reviewers for the informative and constructive feedback. Conflicts of Interest: None of the authors have any conflict of interest.  ... 
doi:10.3390/robotics9030055 fatcat:xzlwllelxjb3zhxn3fpcl6qcci

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Md Islam, Nieb Neom, Md Imtiaz, Sheikh Nooruddin, Md Islam, Md Islam
2019 Ingénierie des Systèmes d'Information  
We also present the taxonomy based on systematic comparisons of existing studies for smartphone-based fall detection solutions.  ...  Thus, researchers are focusing on developing fall detection systems that facilitate the detection and quick rescue of fall victims.  ...  Machine learning based algorithms are not widely supported in all mobile operating systems yet. Thus, threshold based algorithms are easier to port to other operating systems.  ... 
doi:10.18280/isi.240602 fatcat:zroxsfi7xnau3ksoqnfwil6whu

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network

Bo-kyeong Kang, Yelin Han, Jaehoon Oh, Jongwoo Lim, Jongbin Ryu, Myeong Seong Yoon, Juncheol Lee, Soorack Ryu
2022 Journal of Personalized Medicine  
Methods: We performed a retrospective study using adult wrist radiographs.  ...  Results: In total, 702 images were labeled for the segmentation of ten wrist bones.  ...  The Turing test is an important measure of how "intelligent" a deep learning model is.  ... 
doi:10.3390/jpm12050776 fatcat:jd3kmuvvrrcbfng4agvxdump3m

Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs

Franko Hržić, Michael Janisch, Ivan Štajduhar, Jonatan Lerga, Erich Sorantin, Sebastian Tschauner
2021 Mathematics  
In this study, we propose and evaluate a deep learning approach for automatically estimating fracture age.  ...  To address these issues, for the first time, we suggest an automated neural network-based system for determining the age of a pediatric wrist fracture.  ...  Radiologically, digital radiography (DR) and computed tomography (CT) are used for fracture detection, and subsequently for estimating fracture age [2] .  ... 
doi:10.3390/math9243227 fatcat:xzacrdnh7bat3mojqoucnnnchu
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