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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, 3rd Edition

A special issue of COVID (ISSN 2673-8112). This special issue belongs to the section "COVID Clinical Manifestations and Management".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4039

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,

This Special Issue is a continuation of our previous Special Issues “Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19” and “Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, Second Edition.”

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 new variants emerge day by day. This Special Issue will focus on new approaches that 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 handling are of significant value in this regard. We expect a broad range of research ideas, including novel approaches such as statistical machine learning, unsupervised model design, explainable artificial intelligence (XAI), representation learning, reinforcement learning, and more.

N.B.: While the application of artificial intelligence (AI) technologies in scientific research has significantly improved efficiency and accuracy, it has also introduced new forms of academic misconduct, such as data fabrication and text plagiarism, facilitated by AI algorithms. These practices compromise the integrity of research and can lead to misleading scientific guidance. Protecting the integrity of research is crucial to maintaining public trust in science.

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 1200 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 (3 papers)

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Research

18 pages, 8285 KB  
Article
Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region Using Automatic Landmark Detection and Piecewise Affine Warping
by Salvador E. Ayala-Raggi, Rafael Alejandro Cruz-Ovando, Lauro Reyes-Cocoletzi and Aldrin Barreto-Flores
COVID 2026, 6(7), 121; https://doi.org/10.3390/covid6070121 - 8 Jul 2026
Viewed by 396
Abstract
This paper presents an automatic classification system for pulmonary diseases in chest X-rays based on geometric normalization. The proposed method consists of three main modules. Module 1: A landmark detector: A ResNet-18 convolutional neural network with coordinate attention mechanism is trained to predict [...] Read more.
This paper presents an automatic classification system for pulmonary diseases in chest X-rays based on geometric normalization. The proposed method consists of three main modules. Module 1: A landmark detector: A ResNet-18 convolutional neural network with coordinate attention mechanism is trained to predict 15 landmarks defining the lung contour, achieving a mean error of 3.61 pixels (median 3.07 pixels) through an ensemble of four models with test-time augmentation. Module 2: Geometric normalizer: a set of landmarks surrounding the lung region is used to geometrically normalize each image. This normalization involves: Generalized Procrustes Analysis used once to obtain a standard lung shape, Delaunay triangulation to build a deformation mesh, and a piecewise affine transformation (warping) to map the original lung region to a standardized region. This process eliminates variations in position, scale, and orientation in the original set. Module 3: Classifier: normalized images are classified into three categories (COVID-19, Viral Pneumonia, and Normal) using a ResNet-18 classifier with transfer learning and a contrast adjustment (using the SAHS (Statistical Asymmetrical Histogram Stretching) method). The classifier was evaluated through five-fold cross-validation on the COVID-19 Radiography Database, demonstrating high stability with 98.60 ± 0.26% accuracy, and 98.00% F1-Macro, confirming the robustness of the approach. Although the classifier trained with original images reached a higher accuracy than using normalized images, Gradient-weighted Class Activation Mapping (Grad-CAM) analysis and the cropping experiment suggest that this advantage is partly driven by acquisition artifacts rather than lung pathology. In contrast, geometrically normalized images outperform their non-aligned artifact-masked/cropped counterparts: 98.60% vs. 96.24% on the COVID-19 Radiography Database and 94.67% vs. 94.17% on a balanced adult–pediatric mixed dataset including pediatric cases from the Kermany dataset, suggesting that anatomical alignment can yield a more reliable and artifact-resistant representation for pulmonary disease recognition. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, 3rd Edition)
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18 pages, 840 KB  
Article
Utilizing Machine Learning Techniques for Computer-Aided COVID-19 Screening Based on Clinical Data
by Honglun Xu, Andrews T. Anum, Michael Pokojovy, Sreenath Chalil Madathil, Yuxin Wen, Md Fashiar Rahman, Tzu-Liang (Bill) Tseng, Scott Moen and Eric Walser
COVID 2026, 6(1), 17; https://doi.org/10.3390/covid6010017 - 9 Jan 2026
Viewed by 800
Abstract
The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML [...] Read more.
The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, 3rd Edition)
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17 pages, 1190 KB  
Article
Temporal Profiling of SARS-CoV-2 Variants Using BioEnrichPy: A Network-Based Insight into Host Disruption and Neurodegeneration
by Sreelakshmi Kalayakkattil, Ananthakrishnan Anil Indu, Punya Sunil, Haritha Nekkanti, Smitha Shet and Ranajit Das
COVID 2025, 5(12), 203; https://doi.org/10.3390/covid5120203 - 5 Dec 2025
Viewed by 1689
Abstract
SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions [...] Read more.
SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions with host proteins. In this study, we constructed a comprehensive SARS–CoV–2–human protein–protein interaction network and analyzed the temporal evolution of pathway perturbations across different variants. We employed computational approaches, including network-based clustering and functional enrichment analysis, using our custom-developed Python (v3.13) pipeline, BioEnrichPy, to identify key host pathways perturbed by each SARS-CoV-2 variant. Our analyses revealed that while the early variants predominantly targeted respiratory and inflammatory pathways, later variants such as Delta and Omicron exerted more extensive systemic effects, notably impacting neurological and cardiovascular systems. Comparative analyses uncovered distinct, variant-specific molecular adaptations, underscoring the dynamic and evolving nature of SARS-CoV-2–host interactions. Furthermore, we identified host proteins and pathways that represent potential therapeutic vulnerabilities, which appear to have co-evolved with viral mutations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, 3rd Edition)
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