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A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans [article]

Ezequiel de la Rosa, Diana M. Sima, Thijs Vande Vyvere, Jan S. Kirschke, Bjoern Menze
2018 arXiv   pre-print
However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors.  ...  Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI).  ...  INTRODUCTION Traumatic Brain Injury (TBI) is a complex disease process that encompasses a whole spectrum of different pathologies.  ... 
arXiv:1811.05699v1 fatcat:2gp4be723jc6rcg5ln7elgjqzq

Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

Clément Brossard, Benjamin Lemasson, Arnaud Attyé, Jules-Arnaud de Busschère, Jean-François Payen, Emmanuel L. Barbier, Jules Grèze, Pierre Bouzat
2021 Frontiers in Neurology  
The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of  ...  The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more  ...  All authors listed have made a substantial, direct and intellectual contribution to the work, approved it for publication, proofread, and corrected the final manuscript.  ... 
doi:10.3389/fneur.2021.666875 pmid:34177773 pmcid:PMC8222716 fatcat:eytn3fa7erhfxj3nuasabw6pke

Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

Sebastian Röhrich, Johannes Hofmanninger, Lukas Negrin, Georg Langs, Helmut Prosch
2021 European Radiology  
a CT scan within 1 h after the accident.  ...  Results Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax  ...  lung injury in initial and follow-up CT scans to predict ARDS in polytraumatized patients [31] .  ... 
doi:10.1007/s00330-020-07635-6 pmid:33733689 fatcat:pvfqpo5eqzgonl4kxm4abyvfwa

A Preclinical Study on Radiomics-Driven Brain Tumor Prediction Using Deep Convolution Neural Network

Divya S.
2021 Revista GEINTEC  
Radiomics is an exponentially increasing discipline that focuses on mapping the textural details found in various tissues for medical diagnosis.  ...  Researchers created RadSynth, a deep Convolutional Neural Network (CNN) framework that constructs Radiomics images efficiently.  ...  Traumatic Brain Injury (TBI) is really a multifaceted disorder that involves a wide variety of pathologies [1] .  ... 
doi:10.47059/revistageintec.v11i2.1774 fatcat:zlgkau3s5bh2pnqcskneowyz2u

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library.  ...  CT [10575-46] 10575 1C Deep 3D convolution neural network for CT brain hemorrhage classification [10575-47] 10575 1D Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  using diffusion MRI [10134-80] 10134 2G Machine learning algorithm for automatic detection of CT-identifiable hyperdense lesions associated with traumatic brain injury [10134-81] 10134 2H Automatic  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library.  ...  data augmentation technique for brain tumor segmentation [11313-96] 11313 2Q MRI correlates of chronic symptoms in mild traumatic brain injury [11313-97] 11313 2R Development of a 3D carotid atlas  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography

Jia Wang, Xing Xiong, Jing Ye, Yang Yang, Jie He, Juan Liu, Yi-Li Yin
2022 Frontiers in Neuroscience  
A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors.  ...  The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed.  ...  Models were developed in the training cohort and independently tested in the validation cohort. CT Imaging All patients underwent baseline head NECT scan using a GE Gemstone Scanner.  ... 
doi:10.3389/fnins.2022.837041 pmid:35757547 pmcid:PMC9226370 fatcat:m7my5q6fana7jls5xh337dumna

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Jawed Nawabi, Helge Kniep, Sarah Elsayed, Constanze Friedrich, Peter Sporns, Thilo Rusche, Maik Böhmer, Andrea Morotti, Frieder Schlunk, Lasse Dührsen, Gabriel Broocks, Gerhard Schön (+4 others)
2021 Translational Stroke Research  
Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach.  ...  This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH.  ...  To test and evaluate this hypothesis, we employed a radiomics-based ML approach on NECT brain scans of patients presenting with acute primary ICH [17] .  ... 
doi:10.1007/s12975-021-00891-8 pmid:33547592 pmcid:PMC8557152 fatcat:45ebwkctevdnzofr432hl35sna

CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification

Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov, Boris Shirokikh, Anvar Kurmukov, Ivan Blokhin, Valeria Chernina, Alexander Solovev, Victor Gombolevskiy, Sergey Morozov, Mikhail Belyaev
2021 Medical Image Analysis  
Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem.  ...  We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model.  ...  thank Dmitry Petrov (University of Massachusetts Amherst) for valuable comments and Tatiana Korb (Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies) for suggesting test CT  ... 
doi:10.1016/j.media.2021.102054 pmid:33932751 pmcid:PMC8015379 fatcat:4lvgjq224rgbxd3wpcc7j6qz44

CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification [article]

Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov, Boris Shirokikh, Anvar Kurmukov, Ivan Blokhin, Valeria Chernina, Alexander Solovev, Victor Gombolevskiy, Sergey Morozov, Mikhail Belyaev
2020 arXiv   pre-print
Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem.  ...  We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model.  ...  The authors concluded the paper with a call to share data and code to develop an established system for validating and comparing different models collaboratively.  ... 
arXiv:2006.01441v3 fatcat:duopjt4qpngzviopwanvy23by4

Pre-operative imaging and post-operative appearance of standard paediatric neurosurgical approaches: a training guide for neuroradiologists

Mario Ganau, Shailendra A Magdum, Amedeo Calisto
2021 Translational Pediatrics  
Overall, the information presented in a systematic fashion will not only help trainees and fellows to deepen these topics and expand their knowledge in preparation for written and oral boards, but will  ...  A short-cut narrative review was conducted according to the SANRA guidelines to identify studies describing normal and abnormal postoperative radiological features of the most common paediatric neurosurgical  ...  Acknowledgments We are grateful to our graphic artist Dr Lara Prisco (@ PriscoLara) for her drawings which are complementing and enriching the postoperative images presented in this article.  ... 
doi:10.21037/tp-20-484 pmid:34012863 pmcid:PMC8107881 fatcat:qytmhvkq5vgc3fwxj77hg6sdie

3D Deep Learning on Medical Images: A Review

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 Sensors  
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical  ...  Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians.  ...  For example, we may be interested in removing skulls in brain CT scans before feeding to 3D CNN.  ... 
doi:10.3390/s20185097 pmid:32906819 pmcid:PMC7570704 fatcat:top2ambpizdzdpsqamz2xm643u

3D Deep Learning on Medical Images: A Review [article]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
2020 arXiv   pre-print
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical  ...  Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians.  ...  For example, we may be interested in removing skulls in brain CT scans before feeding to 3D CNN.  ... 
arXiv:2004.00218v4 fatcat:iucszcjffnbwbbzc4zzqpbvahy

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods [article]

Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee, Philippe Lambin
2021 arXiv   pre-print
Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow.  ...  Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions.  ...  Midline shift (MLS) in brain is an important characteristic feature that can be used for the diagnosis of traumatic brain injury, brain tumors and some other brain abnormalities [83] .  ... 
arXiv:2111.02398v1 fatcat:glrfdkbcqrbqto2nrl7dnlg3gq
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