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Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis...
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Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19

A special issue of COVID (ISSN 2673-8112).

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 7915

Special Issue Editor

Dr. Somenath Chakraborty

E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Leonard C. Nelson College of Engineering and Sciences, West Virginia University Institute of Technology, Beckley, WV, USA
Interests: artificial intelligence; machine learning; digital image processing; medical AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The design of computational medical diagnosis and prognosis models using state-of-the-art artificial intelligence and machine learning models is a challenging research field, especially in the context of COVID-19 as the new variants emerge day by day. This Special Edition will focus on new approaches which cater to this field of research. The prognosis model should be updated with the most challenging datasets. Data pre-processing, data security, data unbalancing, and big data handing are of significant value in this regard. We expect a broad range of research ideas, including modern new approaches such as statistical machine learning, unsupervised model design, explainable artificial intelligence (XAI), representation learning, reinforcement learning, etc.

Dr. Somenath Chakraborty
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. COVID is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • computational medical diagnosis
  • prognosis model
  • COVID-19

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Published Papers (5 papers)

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Research

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9 pages, 1467 KiB  
Article
Classification of High-Resolution Chest CT Scan Images Using Adaptive Fourier Neural Operators for COVID-19 Diagnosis
by Anusha Gurrala, Krishan Arora, Himanshu Sharma, Shamimul Qamar, Ajay Roy and Somenath Chakraborty
COVID 2024, 4(8), 1236-1244; https://doi.org/10.3390/covid4080088 - 7 Aug 2024
Viewed by 913
Abstract
In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT [...] Read more.
In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO’s superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19)
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15 pages, 1537 KiB  
Article
3Cs: Unleashing Capsule Networks for Robust COVID-19 Detection Using CT Images
by Rawan Alaufi, Felwa Abukhodair and Manal Kalkatawi
COVID 2024, 4(8), 1113-1127; https://doi.org/10.3390/covid4080077 - 24 Jul 2024
Viewed by 721
Abstract
The COVID-19 pandemic has spread worldwide for over two years. It was considered a significant threat to global health due to its transmissibility and high pathogenicity. The standard test for COVID-19, namely, reverse transcription polymerase chain reaction (RT–PCR), is somehow inaccurate and might [...] Read more.
The COVID-19 pandemic has spread worldwide for over two years. It was considered a significant threat to global health due to its transmissibility and high pathogenicity. The standard test for COVID-19, namely, reverse transcription polymerase chain reaction (RT–PCR), is somehow inaccurate and might have a high false-negative rate (FNR). As a result, an infected person with a negative test result may unknowingly continue to spread the virus, especially if they are infected with an undiscovered COVID-19 strain. Thus, a more accurate diagnostic technique is required. In this study, we propose 3Cs, which is a capsule neural network (CapsNet) used to classify computed tomography (CT) images as novel coronavirus pneumonia (NCP), common pneumonia (CP), or normal lungs. Using 6123 CT images of healthy patients’ lungs and those of patients with CP and NCP, the 3Cs method achieved an accuracy of around 98% and an FNR of about 2%, demonstrating CapNet’s ability to extract features from CT images that distinguish between healthy and infected lungs. This research confirmed that using CapsNet to detect COVID-19 from CT images results in a lower FNR compared to RT–PCR. Thus, it can be used in conjunction with RT–PCR to diagnose COVID-19 regardless of the variant. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19)
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19 pages, 7026 KiB  
Article
An Energy-Optimized Artificial Intelligence of Things (AIoT)-Based Biosensor Networking for Predicting COVID-19 Outbreaks in Healthcare Systems
by Monika Pahuja and Dinesh Kumar
COVID 2024, 4(6), 696-714; https://doi.org/10.3390/covid4060047 - 30 May 2024
Viewed by 1206
Abstract
By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today’s environment, driven by the proliferation of advanced [...] Read more.
By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today’s environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices’ limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model’s efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19)
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11 pages, 394 KiB  
Article
Symptoms Predicting SARS-CoV-2 Test Results in Resident Physicians and Fellows in New York City
by Tania P. Chen, Meizhen Yao, Vishal Midya, Betty Kolod, Rabeea F. Khan, Adeyemi Oduwole, Bernard Camins, I. Michael Leitman, Ismail Nabeel, Kristin Oliver and Damaskini Valvi
COVID 2023, 3(5), 671-681; https://doi.org/10.3390/covid3050049 - 25 Apr 2023
Viewed by 1619
Abstract
Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in [...] Read more.
Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in physician trainees from a large healthcare system in New York. We used survey data on symptoms history and SARS-CoV-2 testing results collected retrospectively from 328 physician trainees in the Mount Sinai Health System, over the period 1 February 2020 to 31 July 2020. Prospective data on symptoms reported prior to SARS-CoV-2 test results were available from the employee health service COVID-19 registry for 186 trainees and analyzed to confirm absence of recall bias. We estimated the associations between symptoms and IgG antibody and/or reverse transcriptase polymerase chain reaction test results using Bayesian generalized linear mixed effect regression models adjusted for confounders. We identified symptoms predicting a positive SARS-CoV-2 test result using extreme gradient boosting (XGBoost). Cough, chills, fever, fatigue, myalgia, headache, shortness of breath, diarrhea, nausea/vomiting, loss of smell, loss of taste, malaise and runny nose were associated with a positive SARS-CoV-2 test result. Loss of taste, myalgia, loss of smell, cough and fever were identified as key predictors for a positive SARS-CoV-2 test result in the XGBoost model. Inclusion of sociodemographic and occupational risk factors in the model improved prediction only slightly (from AUC = 0.822 to AUC = 0.838). Loss of taste, myalgia, loss of smell, cough and fever are key predictors for symptom-based screening of SARS-CoV-2 infection in healthcare settings with remote screening and/or limited testing capacity. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19)
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Review

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15 pages, 757 KiB  
Review
A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread
by Liege Cheung, Adela S. M. Lau, Kwok Fai Lam and Pauline Yeung Ng
COVID 2024, 4(4), 466-480; https://doi.org/10.3390/covid4040031 - 11 Apr 2024
Viewed by 1637
Abstract
Contact tracing is a method used to control the spread of a pandemic. The objectives of this research are to conduct an empirical review and content analysis to identify the environmental factors causing the spread of the pandemic and to propose an ontology-based [...] Read more.
Contact tracing is a method used to control the spread of a pandemic. The objectives of this research are to conduct an empirical review and content analysis to identify the environmental factors causing the spread of the pandemic and to propose an ontology-based big data architecture to collect these factors for prediction. No research studies these factors as a whole in pandemic prediction. The research method used was an empirical study and content analysis. The keywords contact tracking, pandemic spread, fear, hygiene measures, government policy, prevention programs, pandemic programs, information disclosure, pandemic economics, and COVID-19 were used to archive studies on the pandemic spread from 2019 to 2022 in the EBSCOHost databases (e.g., Medline, ERIC, Library Information Science & Technology, etc.). The results showed that only 84 of the 588 archived studies were relevant. The risk perception of the pandemic (n = 14), hygiene behavior (n = 7), culture (n = 12), and attitudes of government policies on pandemic prevention (n = 25), education programs (n = 2), business restrictions (n = 2), technology infrastructure, and multimedia usage (n = 24) were the major environmental factors influencing public behavior of pandemic prevention. An ontology-based big data architecture is proposed to collect these factors for building the spread prediction model. The new method overcomes the limitation of traditional pandemic prediction model such as Susceptible-Exposed-Infected-Recovered (SEIR) that only uses time series to predict epidemic trend. The big data architecture allows multi-dimension data and modern AI methods to be used to train the contagion scenarios for spread prediction. It helps policymakers to plan pandemic prevention programs. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19)
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