research-article

CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning

Authors:

Manvel Avetisian,

Konstantin Egorov,

Aleksandr Nesterov,

Aleksandr Nikolaev,

Alexander Ponomarchuk,

Elena Sokolova,

Dmitry UmerenkovAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 12, Issue 4

Article No.: 28, Pages 1 - 16

https://doi.org/10.1145/3467471

Published: 08 September 2021 Publication History

Abstract

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizing patients by severity of the disease. In this article we adopted an approach based on using an ensemble of deep convolutional neural networks for segmentation of slices of lung CT scans. Using our models, we are able to segment the lesions, evaluate patients’ dynamics, estimate relative volume of lungs affected by lesions, and evaluate the lung damage stage. Our models were trained on data from different medical centers. We compared predictions of our models with those of six experienced radiologists, and our segmentation model outperformed most of them. On the task of classification of disease severity, our model outperformed all the radiologists.

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Cited By

Saiyeda ABadar MTomar DBadar LAlam M(2024)Interpretation and validation of COVID-19 data obtained from Artificial IntelligenceDiagnosis and Analysis of COVID-19 Using Artificial Intelligence and Machine Learning-based Techniques10.1016/B978-0-323-95374-0.00002-6(371-379)Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95374-0.00002-6
Portela ECortes Oda Silva J(2023)A rapid literature review on ensemble algorithms for COVID-19 classification using image-based examsInternational Journal of Hybrid Intelligent Systems10.3233/HIS-23000919:3,4(129-143)Online publication date: 3-Nov-2023
https://doi.org/10.3233/HIS-230009
Bartenschlager CEbel SKling SVehreschild JZabel LSpinner CSchuler AHeller ABorgmann SHoffmann RRieg SMessmann HHower MBrunner JHanses FRömmele C(2023)COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German HospitalsACM Transactions on Management Information Systems10.1145/356743114:2(1-16)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3567431
Show More Cited By

Index Terms

CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 12, Issue 4

December 2021

225 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3483349

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2021

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 September 2020

Published in TMIS Volume 12, Issue 4

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Cited By

Saiyeda ABadar MTomar DBadar LAlam M(2024)Interpretation and validation of COVID-19 data obtained from Artificial IntelligenceDiagnosis and Analysis of COVID-19 Using Artificial Intelligence and Machine Learning-based Techniques10.1016/B978-0-323-95374-0.00002-6(371-379)Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95374-0.00002-6
Portela ECortes Oda Silva J(2023)A rapid literature review on ensemble algorithms for COVID-19 classification using image-based examsInternational Journal of Hybrid Intelligent Systems10.3233/HIS-23000919:3,4(129-143)Online publication date: 3-Nov-2023
https://doi.org/10.3233/HIS-230009
Bartenschlager CEbel SKling SVehreschild JZabel LSpinner CSchuler AHeller ABorgmann SHoffmann RRieg SMessmann HHower MBrunner JHanses FRömmele C(2023)COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German HospitalsACM Transactions on Management Information Systems10.1145/356743114:2(1-16)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3567431
Portela ECortes Oda Silva J(2023)A Rapid Review on Ensemble Algorithms for COVID-19 Classification Using Image-Based ExamsIntelligent Systems Design and Applications10.1007/978-3-031-27440-4_10(96-106)Online publication date: 31-May-2023
https://doi.org/10.1007/978-3-031-27440-4_10
Ponomarchuk ABurenko IMalkin ENazarov IKokh VAvetisian MZhukov L(2022)Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and CoughIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2022.314251416:2(175-187)Online publication date: Feb-2022
https://doi.org/10.1109/JSTSP.2022.3142514
Zhao KZhang QYuan STsoi K(2021)Introduction to the Special Section on Using AI and Data Science to Handle Pandemics and Related DisruptionsACM Transactions on Management Information Systems10.1145/348696912:4(1-2)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3486969

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