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AI-Driven quantification, staging and outcome prediction of COVID-19 pneumonia

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard (+23 others)
2020 Medical Image Analysis  
Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly.  ...  imaging biomarkers with clinical and biological attributes.  ...  DIC20161236437), the Swiss National Science Foundation Grant no. 188153 and benefited from methodological developments done in the context of Dr.  ... 
doi:10.1016/j.media.2020.101860 pmid:33171345 pmcid:PMC7558247 fatcat:iz5zcq7ftzg7lendm563asl7fu

Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients

Anđela Blagojević, Tijana Šušteršič, Ivan Lorencin, Sandi Šegota, Dragan Milovanović, Danijela Baskić, Dejan Baskić, Zlatan Car, Nenad Filipović
2021 EAI Endorsed Transactions on Bioengineering and Bioinformatics  
infected with COVID-19.  ...  METHODS: The methodology combines several aspects (1) classification of patients into several classes of clinical condition (2) segmentation of human lungs in X ray images (3) finite element simulation  ...  First of all, five blood biomarkers that most reliably described the development of COVID-19 disease in patients were selected as the features, according to the previously described methodology.  ... 
doi:10.4108/eai.12-3-2021.169028 fatcat:gc6wzihljjcx3ls7ul7ycckadm

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19 [article]

Deisy Morselli Gysi and Ítalo Do Valle and Marinka Zitnik and Asher Ameli and Xiao Gan and Onur Varol and Susan Dina Ghiassian and JJ Patten and Robert Davey and Joseph Loscalzo and Albert-László Barabási
2020 arXiv   pre-print
of drugs with potential COVID-19 efficacy.  ...  This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the  ...  Proximity and Diffusion bases methods explore the PPI in a much vast and diverse way than the AI methods (C) Methods inside the same pipeline tend to select similar genes, the similarity of selected genes  ... 
arXiv:2004.07229v2 fatcat:uq5uvmapqjbi5cqoo2etk3c3rm

Obstructive Sleep Apnea (OSA) and COVID-19: Mortality Prediction of COVID-19-Infected Patients with OSA Using Machine Learning Approaches

Sidratul Tanzila Tasmi, Md. Mohsin Sarker Raihan, Abdullah Bin Shams
2022 COVID  
COVID-19, or coronavirus disease, has caused an ongoing global pandemic causing un-precedented damage in all scopes of life.  ...  The comobordities of OSA with high blood pressure, diabetes, obesity, and age can place the life of an already infected COVID-19 patient into danger.  ...  In the oversampling technique of SMOTE used in the code, the examples that are close to the feature space are selected which are connected to form a line segment with all the records in between.  ... 
doi:10.3390/covid2070064 fatcat:t3i2waw4qze5xjn7vwxvyczeru

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Lu Huo, Jiao Jiao Li, Ling Chen, Zuguo Yu, Gyorgy Hutvagner, Jinyan Li
2021 Briefings in Bioinformatics  
This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic.  ...  We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods  ...  Matrix factorization analysis Matrix factorization-based approaches are unsupervised machine learning methods for simultaneous data integration and dimensional reduction.  ... 
doi:10.1093/bib/bbab229 pmid:34111889 pmcid:PMC8344433 fatcat:n5wva7mjqzfsldiahwzn2cun5e

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning [article]

Bruce Patterson, Jose Guevara-Coto, Ram Yogendra, Edgar B. Francisco, emily long, Amruta Pise, Hallison Rodrigues, Purvi Parikh, Javier Mora, Rodrigo A. Mora-Rodriguez
2020 bioRxiv   pre-print
Individuals with systemic symptoms long after COVID-19 has cleared represent approximately 10% of all COVID-19 infected individuals.  ...  We collected plasma and isolated PBMCs from 29 normal individuals, 26 individuals with mild-moderate COVID-19, 25 individuals with severe COVID-19, and 64 individuals with Chronic COVID-19 symptoms.  ...  Some 239 of these molecules have been proposed as biomarkers to monitor the clinical evolution 240 and to determine treatment selection for COVID-19 patients.  ... 
doi:10.1101/2020.12.16.423122 fatcat:lhfqecqfkrfgfdycyqz3wzpgae

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

Vitoantonio Bevilacqua, Nicola Altini, Berardino Prencipe, Antonio Brunetti, Laura Villani, Antonello Sacco, Chiara Morelli, Michele Ciaccia, Arnaldo Scardapane
2021 Electronics  
The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients.  ...  In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE).  ...  To the best of our knowledge, the possibility to characterize lung parenchyma and lesions with radiomics methodologies from COVID-19 patients, with respect to the onset of PTE, has never been explored  ... 
doi:10.3390/electronics10202475 fatcat:uz4lx4jiezhxtgncmn5c662zea

Deep Learning applications for COVID-19

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021 Journal of Big Data  
AbstractThis survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19.  ...  Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.  ...  Opinions, findings, conclusions, or recommendations in this paper are the authors' and do not reflect the views of the NSF.  ... 
doi:10.1186/s40537-020-00392-9 pmid:33457181 pmcid:PMC7797891 fatcat:aokxo63z2rhdpfxo3egyf3xpcm

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

Youssoufa Mohamadou, Aminou Halidou, Pascalin Tiam Kapen
2020 Applied intelligence (Boston)  
Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.  ...  The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-  ...  Compliance with Ethical Standards Conflict of interests The authors declare that they have no conflict of interest.  ... 
doi:10.1007/s10489-020-01770-9 pmid:34764546 pmcid:PMC7335662 fatcat:sxoc44h6bvhfjccdimeyfgchh4

Development and Evaluation of an AI System for COVID-19 [article]

Cheng Jin, Weixiang Chen, Yukun Cao, Zhanwei Xu, Xin Zhang, Lei Deng, Chuansheng Zheng, Jie Zhou, Heshui Shi, Jianjiang Feng
2020 medRxiv   pre-print
Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease.  ...  With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts.  ...  We found that all features selected to distinguish CAP and COVID-19 was significant in t-test.  ... 
doi:10.1101/2020.03.20.20039834 fatcat:e3louifnhvcobbt2blkuoivx7m

A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Noushin Anan, Rafidah Zainon, Mahbubunnabi Tamal
2022 Insights into Imaging  
Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field.  ...  AbstractRadiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images.  ...  Acknowledgements The authors would like to thank Universiti Sains Malaysia and Deputyship for Research & Innovation, Ministry of Education Kingdom of Saudi Arabia for the financial assistance through International  ... 
doi:10.1186/s13244-021-01153-9 pmid:35124733 pmcid:PMC8817778 fatcat:ij7h6fjnyrfvfpxp2ffhloghti

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Cheng Jin, Weixiang Chen, Yukun Cao, Zhanwei Xu, Zimeng Tan, Xin Zhang, Lei Deng, Chuansheng Zheng, Jie Zhou, Heshui Shi, Jianjiang Feng
2020 Nature Communications  
We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system.  ...  Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease.  ...  This study was supported by Zhejiang University special scientific research fund for COVID-19 prevention and control.  ... 
doi:10.1038/s41467-020-18685-1 pmid:33037212 fatcat:323jsymv3jbefcfejlm2y4yy3e

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Mazin Abed Mohammed, Belal Al-Khateeb, Mohammed Yousif, Salama A. Mostafa, Seifedine Kadry, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Mohammed A. A. Al qaness
2022 Computational Intelligence and Neuroscience  
This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis.  ...  The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria.  ...  A clinical computer-aided diagnosis (CAD) system in the study by [30] applies automatic discrimination of COVID-19 from non-COVID-19 pneumonia patients using CT features.  ... 
doi:10.1155/2022/1307944 pmid:35996653 pmcid:PMC9392599 fatcat:oezflbi3yzeqdgjztgqou4sfpm

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

H. Swapnarekha, Himansu Sekhar Behera, Janmenjoy Nayak, Bighnaraj Naik
2020 Chaos, Solitons & Fractals  
In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done.  ...  and forecasting of COVID-19 real time data.  ...  The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full).  ... 
doi:10.1016/j.chaos.2020.109947 pmid:32836916 pmcid:PMC7256553 fatcat:uytoc2bfung7hhw47z2rnbiw5q

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
for medical image analysis applications based on the type of generated explanations and technical similarities.  ...  Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow.  ...  Latent Space Interpretation The latent space is used to uncover the salient factors of variation learned in the data with respect to the clinical knowledge.  ... 
arXiv:2111.02398v1 fatcat:glrfdkbcqrbqto2nrl7dnlg3gq
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