Literature DB >> 36010204

A Comprehensive Review of Machine Learning Used to Combat COVID-19.

Rahul Gomes1, Connor Kamrowski1, Jordan Langlois1, Papia Rozario2, Ian Dircks1, Keegan Grottodden1, Matthew Martinez1, Wei Zhong Tee1, Kyle Sargeant1, Corbin LaFleur1, Mitchell Haley1.   

Abstract

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

Entities:  

Keywords:  COVID-19 prognosis; CT scan; X-rays; deep learning; machine learning

Year:  2022        PMID: 36010204      PMCID: PMC9406981          DOI: 10.3390/diagnostics12081853

Source DB:  PubMed          Journal:  Diagnostics (Basel)        ISSN: 2075-4418


  87 in total

1.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.

Authors:  Ying Song; Shuangjia Zheng; Liang Li; Xiang Zhang; Xiaodong Zhang; Ziwang Huang; Jianwen Chen; Ruixuan Wang; Huiying Zhao; Yutian Chong; Jun Shen; Yunfei Zha; Yuedong Yang
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2021-12-08       Impact factor: 3.710

2.  Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

Authors:  Edward H Lee; Jimmy Zheng; Errol Colak; Maryam Mohammadzadeh; Golnaz Houshmand; Nicholas Bevins; Felipe Kitamura; Emre Altinmakas; Eduardo Pontes Reis; Jae-Kwang Kim; Chad Klochko; Michelle Han; Sadegh Moradian; Ali Mohammadzadeh; Hashem Sharifian; Hassan Hashemi; Kavous Firouznia; Hossien Ghanaati; Masoumeh Gity; Hakan Doğan; Hojjat Salehinejad; Henrique Alves; Jayne Seekins; Nitamar Abdala; Çetin Atasoy; Hamidreza Pouraliakbar; Majid Maleki; S Simon Wong; Kristen W Yeom
Journal:  NPJ Digit Med       Date:  2021-01-29

3.  Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

Authors:  Nathalie Lassau; Samy Ammari; Emilie Chouzenoux; Hugo Gortais; Paul Herent; Matthieu Devilder; Samer Soliman; Olivier Meyrignac; Marie-Pauline Talabard; Jean-Philippe Lamarque; Remy Dubois; Nicolas Loiseau; Paul Trichelair; Etienne Bendjebbar; Gabriel Garcia; Corinne Balleyguier; Mansouria Merad; Annabelle Stoclin; Simon Jegou; Franck Griscelli; Nicolas Tetelboum; Yingping Li; Sagar Verma; Matthieu Terris; Tasnim Dardouri; Kavya Gupta; Ana Neacsu; Frank Chemouni; Meriem Sefta; Paul Jehanno; Imad Bousaid; Yannick Boursin; Emmanuel Planchet; Mikael Azoulay; Jocelyn Dachary; Fabien Brulport; Adrian Gonzalez; Olivier Dehaene; Jean-Baptiste Schiratti; Kathryn Schutte; Jean-Christophe Pesquet; Hugues Talbot; Elodie Pronier; Gilles Wainrib; Thomas Clozel; Fabrice Barlesi; Marie-France Bellin; Michael G B Blum
Journal:  Nat Commun       Date:  2021-01-27       Impact factor: 14.919

4.  COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning.

Authors:  Hayden Gunraj; Ali Sabri; David Koff; Alexander Wong
Journal:  Front Med (Lausanne)       Date:  2022-03-10

5.  C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing.

Authors:  Neha Rajawat; Bharat Singh Hada; Mayank Meghawat; Soniya Lalwani; Rajesh Kumar
Journal:  Arab J Sci Eng       Date:  2022-04-30       Impact factor: 2.807

6.  Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

Authors:  Dheyaa Ahmed Ibrahim; Dilovan Asaad Zebari; Hussam J Mohammed; Mazin Abed Mohammed
Journal:  Expert Syst       Date:  2022-05-01       Impact factor: 2.812

7.  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).

Authors:  Shuai Wang; Bo Kang; Jinlu Ma; Xianjun Zeng; Mingming Xiao; Jia Guo; Mengjiao Cai; Jingyi Yang; Yaodong Li; Xiangfei Meng; Bo Xu
Journal:  Eur Radiol       Date:  2021-02-24       Impact factor: 5.315

8.  Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.

Authors:  Vinayakumar Ravi; Harini Narasimhan; Chinmay Chakraborty; Tuan D Pham
Journal:  Multimed Syst       Date:  2021-07-06       Impact factor: 2.603

9.  Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Authors:  Laure Wynants; Ben Van Calster; Gary S Collins; Richard D Riley; Georg Heinze; Ewoud Schuit; Marc M J Bonten; Darren L Dahly; Johanna A A Damen; Thomas P A Debray; Valentijn M T de Jong; Maarten De Vos; Paul Dhiman; Maria C Haller; Michael O Harhay; Liesbet Henckaerts; Pauline Heus; Michael Kammer; Nina Kreuzberger; Anna Lohmann; Kim Luijken; Jie Ma; Glen P Martin; David J McLernon; Constanza L Andaur Navarro; Johannes B Reitsma; Jamie C Sergeant; Chunhu Shi; Nicole Skoetz; Luc J M Smits; Kym I E Snell; Matthew Sperrin; René Spijker; Ewout W Steyerberg; Toshihiko Takada; Ioanna Tzoulaki; Sander M J van Kuijk; Bas van Bussel; Iwan C C van der Horst; Florien S van Royen; Jan Y Verbakel; Christine Wallisch; Jack Wilkinson; Robert Wolff; Lotty Hooft; Karel G M Moons; Maarten van Smeden
Journal:  BMJ       Date:  2020-04-07
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