ISSN: 2709-4111

Covid19: Challenging Issues - Methods and Evaluation

Issue 2, 2021

Editor(s)-in-Chief: Nikolaos Bourbakis, Jeffrey P. Tsai and Nenad Filipovic

Articles
Information

Editorial
Appears in:
bebi 21(2): e1

Authors:
N. G. Bourbakis, Jeffrey P. Tsai, N. Filipovic

Abstract:
more »
Texture-based Feature Extraction for COVID-19 Pneumonia Classification using Chest Radiography
Appears in:
bebi 21(2): e2

Authors:
L. V. Moura, C. M. Dartora, C. Mattjie, R. C. Barros, A. M. Marques da Silva

Abstract:
INTRODUCTION: The identification of COVID-19 pneumonia using chest radiography is challenging. OBJECTIVES: We investigate classification models to differentiate COVID-19-based and typical pneumonia in chest radiography. METHODS: We use 136 segmented chest X-rays to train and eval…INTRODUCTION: The identification of COVID-19 pneumonia using chest radiography is challenging.

OBJECTIVES: We investigate classification models to differentiate COVID-19-based and typical pneumonia in chest radiography.

METHODS: We use 136 segmented chest X-rays to train and evaluate the performance of support vector machine (SVM), random forest (RF), AdaBoost (AB), and logistic regression (LR) classification methods. We use the PyRadiomics to extract statistical texture-based features in the right, left, and in six lung zones. We use a stratified k-folds (k=5) cross-validation within the training dataset, selecting the most relevant features with validation accuracy and relative feature importance.

RESULTS: The AB model seems to be the best discriminant method, using six lung zones (AUC = 0.98).

CONCLUSION: Our study shows a predominance of radiomic texture-based features related to COVID-19 pneumonia within the right lung, with a tendency within the upper lung zone.
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Modelling COVID-19 using Fuzzy Cognitive Maps (FCM)
Appears in:
bebi 21(2): e3

Author:
Peter P. Groumpos

Abstract:
INTRODUCTION: The outbreak of COVID-19 has gained ground in many countries, leading towards a global health emergency. Research efforts have been intensified all around the humankind. OBJECTIVE: The main objective of this paper is to study the new pandemic COVID-19 using for the first-time theor…INTRODUCTION: The outbreak of COVID-19 has gained ground in many countries, leading towards a global health emergency. Research efforts have been intensified all around the humankind.

OBJECTIVE: The main objective of this paper is to study the new pandemic COVID-19 using for the first-time theories of Fuzzy Cognitive Maps (FCM).

METHODS: All known studies for COVID-19 are done based on statistical models. These statistical approaches depend solely on correlation factors. The factor of causality has not been considered due to the luck of sufficient mathematical models based on causality. Correlation does not imply causality while causality always implies correlation. The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, to investigate the whole spectrum of COVID-19.

RESULTS: Early theoretical simulation studies using a COVID-19 FCM model have been conducted. Simulations with real patient data give excellent results. The state space Advanced Fuzzy Cognitive Maps (AFCM) is the natural sequence of the classical FCM theories.

CONCLUSIONS: This study gives strong evidence that the “generic FCM theories” are probably the only ones that explore the causality between the variables of medical problems in a sound mathematical and scientific foundation.
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Robust Heatmap Template Generation for COVID-19 Biomarker Detection
Appears in:
bebi 21(2): e4

Authors:
Mirtha Lucas, Miguel Lerma, Jacob Furst, Daniela Raicu

Abstract:
INTRODUCTION: Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. OBJECTIVES: The purpose of this work is to develop a technique for detecting biomarkers of four possibl…INTRODUCTION: Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis.

OBJECTIVES: The purpose of this work is to develop a technique for detecting biomarkers of four possible conditions in chest X-rays, and study the patterns arising from the location of biomarkers.

METHODS: We use transfer learning applied to a pretrained VGG19 neural network to build a model capable of detecting the four conditions in chest X-rays. For biomarkers detection we use Grad-CAM. Patterns in the biomarkers are found by using classical eigenfaces approach.

RESULTS: The discovered patterns are consistent across images from a given class of disease, and they are robust with respect to changes in dataset.

CONCLUSION: The identified patterns can serve as biomarkers for a given disease in chest X-ray images, and constitute explanations of how the deep learning model makes classification decisions.
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Safe in COVID-19: A platform to support effective monitoring of incidents during a pandemic
Appears in:
bebi 21(2): e5

Authors:
D.G. Katehakis, G. Kavlentakis, S. Kostomanolakis, F. Logothetidis, Y. Petrakis, N. Stathiakis, V. Tzikoulis, H. Kondylakis, A. Kouroubali

Abstract:
INTRODUCTION: The ongoing coronavirus pandemic is affecting the lives of millions of people, changing society by enacting new rules for social life, business and travel. The world community (i.e. international organizations, public administrations, businesses, and citize…INTRODUCTION: The ongoing coronavirus pandemic is affecting the lives of millions of people, changing society by enacting new rules for social life, business and travel. The world community (i.e. international organizations, public administrations, businesses, and citizens) has conducted a huge effort in delivering digital solutions to properly address the challenges imposed. A multitude of approaches have been followed, towards achieving a wide spectrum of side goals.

OBJECTIVES: This work presents a platform, designed for public health authorities, to effectively track suspect, probable and confirmed incidence cases in a pandemic by means of a mobile app used by citizens to provide immediate feedback.The aim has been to better support human tracers, and the platform must not be confused with proximity tracking apps.

METHODS: The outbreak response tool was developed based on official information and guidelines, on top of an already existing personal health record app which has been extended, to properly accommodate specific needs that emerged during the crisis.

RESULTS: The developed platform provides the framework to support return to the “new normal” in less time, with reduced stress and more security for individuals, more direct and safer management of patients by physicians, and better possibilities for monitoring the epidemic by public health authorities. Issues relevant to privacy concerns, and interoperability with available patient registries and data analytics tools were also examined to better support public healthcare delivery and contain the spread of the infection.

CONCLUSION: In order for the foreseen benefits to be realized, there is a need to respect safety and security regulations, while at the same time conform to international standards and widely accepted medical protocols. Cross-border interoperability and the availability of appropriate links (i.e. publicly available interfaces) to relevant open data and national registries is considered to be of paramount importance.
more »
Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients
Appears in:
bebi 21(2): e6

Authors:
Anđela Blagojević, Tijana Šušteršič, Ivan Lorencin, Sandi Baressi Šegota, Dragan Milovanović, Danijela Baskić, Dejan Baskić, Zlatan Car, Nenad Filipović

Abstract:
INTRODUCTION: Machine learning algorithms and in silico models for the COVID-19 have been used to classify infectious people and predict their condition in time. OBJECTIVES: This study aims at creating a personalized model that combines machine learning and finite element simulation approach in…INTRODUCTION: Machine learning algorithms and in silico models for the COVID-19 have been used to classify infectious people and predict their condition in time.

OBJECTIVES: This study aims at creating a personalized model that combines machine learning and finite element simulation approach in order to predict development of COVID-19 infection in patients.

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 to investigate the spreading of SARS-COV-2 virion in the lungs.

RESULTS: The findings show accuracy larger than 90% in all aspects of methodology. FE simulation has revealed that the distribution of airflow in the lung changes in time with the infection.

CONCLUSION: The key benefit of our proposed method is that it combines several methods that will be further improved in order to create a truly unique combined methodology for predictive models in patients infected with COVID-19.
more »

Scope

EAI Endorsed Transactions on Bioengineering and Bioinformatics (BEBI), formerly EAI Endorsed Transactions on Ambient Systems, aims at building intelligent synergies and systems among Bioinformatics, Computational Biology, Bioengineering and Biomedicine these complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design. Research and development in these areas are impacting the science and technology in fields such as medicine, food production, forensics, etc. by advancing fundamental concepts in molecular biology, by helping us understand living organisms at multiple levels, by developing innovative implants and bio-prosthetics, and by improving tools and techniques for the detection, prevention and treatment of diseases. The BEBI will provide a common platform for the cross fertilization of ideas, and for shaping knowledge and scientific achievements by bridging these very important and complementary disciplines into an interactive and attractive forum.

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[OCLC Discovery Services](https://www.worldcat.org/search?q=so%3AEAI+endorsed+transactions+on+Bioengineering+and+Bioinformatics&qt=results…

Editors-in-Chief
Nikolaos Bourbakis, Wright State University in Ohio, United States
Jeffrey P. Tsai, Asia University, Taiwan
Nenad Filipovic, University of Kragujevac, Serbia
Editorial board
Benny Lo (Imperial College London, UK)
Henrique C Fernandes (Federal University of Uberlandia,…

Editors-in-Chief
Nikolaos Bourbakis, Wright State University in Ohio, United States
Jeffrey P. Tsai, Asia University, Taiwan
Nenad Filipovic, University of Kragujevac, Serbia
Editorial board
Benny Lo (Imperial College London, UK)
Henrique C Fernandes (Federal University of Uberlandia, Brazil)
Koji Kashihara (Ritsumeikan University, Japan)
Kostas Michalopoulos (Convergent Engineering Inc., USA)
Luca Citi (University of Essex, UK)
George Lambrou (Kapodistriakon University of Athens, Greece)
Constantinos Pattichis (University of Cyprus, Cyprus)
Nikolaos Tsekos (University of Houston, USA)
Athanasios Tsanas (University of Edinburgh, UK)
Alexandros Karargyris (IBM, USA)
Iosif Papadakis-Ktistakis (ASML, USA)
Ioannis Pavlidis (University of Houston, USA)
Michael Raymer (Wright State University, USA)
Katia Del Rio-Tsonis (Miami University, USA)
Pothitos Pytichoutis (University of Dayton, USA)
Sameer Antani (National Institutes of Health, USA)
Michalis Zervakis (Technical University of Crete, Greece)
John R. Rose (University of South Carolina, USA)
George M. Spyrou (The Cyprus School of Molecular Medicine, Cyprus)
Roman Snytsar (Research and AI Microsoft, USA)
Fani Deligianni (Glasgow University, Scotland)
Kristen Beck (IBM Research, USA)
Ehsan Dehghan (IBM Research, Canada)
Zoran Bosnic (University of Ljubljana, Slovenia)
Marko Robnik-Sikonja (University of Ljubljana, Slovenia)
Ka-Lok Ng (Asia University, Taiwan)
Tijana Sustersic (Faculty of Engineering, University of Kragujevac)
Zoran Bosnić (University of Ljubljana, Slovenia)
Marko Robnik-Šikonja (University of Ljubljana, Slovenia)

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/bebi/index

Publisher: EAI
ISSN: 2709-4111
Volume: 1

Published: 2021-03-24

Editorial

Texture-based Feature Extraction for COVID-19 Pneumonia Classification using Chest Radiography

Modelling COVID-19 using Fuzzy Cognitive Maps (FCM)

Robust Heatmap Template Generation for COVID-19 Biomarker Detection

Safe in COVID-19: A platform to support effective monitoring of incidents during a pandemic

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

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