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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, Second 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: 28 March 2025 | Viewed by 1964

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,

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

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 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 (2 papers)

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Research

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13 pages, 578 KiB  
Article
Mental Health Symptom Reporting to a Virtual Triage Engine Prior to and During the COVID-19 Pandemic
by George A. Gellert, Aleksandra Kabat-Karabon, Tim Price, Gabriel L. Gellert, Kacper Kuszczyński, Mateusz Nowak and Piotr M. Orzechowski
COVID 2024, 4(12), 1908-1920; https://doi.org/10.3390/covid4120134 - 29 Nov 2024
Viewed by 595
Abstract
Objective: To examine patient-user symptom reporting to an AI-based online virtual triage (VT) and care-referral engine to assess patterns of mental health symptoms (MHS) reporting prior to and during the COVID-19 pandemic. Methods: The frequencies of 11 MHS reported through VT were analyzed [...] Read more.
Objective: To examine patient-user symptom reporting to an AI-based online virtual triage (VT) and care-referral engine to assess patterns of mental health symptoms (MHS) reporting prior to and during the COVID-19 pandemic. Methods: The frequencies of 11 MHS reported through VT were analyzed over three time intervals: one year prior to the WHO declaring a global COVID-19 emergency; from pandemic declaration to a mid-point in US vaccine distribution/uptake; and one year thereafter. Results: A total of 4,346,987 VT encounters/interviews presenting somatic and MHS occurred, increasing over time and peaking in the COVID-19 post-vaccine interval with 2,257,553 encounters (51.9%). In 866,218 encounters (19.9%), at least one MHS was reported. MHS reporting declined across subsequent time intervals, was lowest in the COVID-19 post-vaccine period (19.1%), and slightly higher in the pre-pandemic and COVID-19 pre-vaccine intervals (p = 0.05). The most frequently reported symptoms were anxiety, sleep disorder, general anxiety, irritability, and nervousness. Women reported anxiety less often and nervousness and irritability more often. Individuals aged 60+ years reported anxiety and nervousness less frequently, insomnia and sleep disorder more often than individuals 18–39 and 40–59 years old, and sleep disorder more often than those aged 40–59 years in all periods (all p = 0.05). Conclusions: Overall VT usage for somatic and mental health symptom reporting and care referral increased dramatically during the pandemic. VT effectively screened and provided care referral for patient-users presenting with MHS. Virtual triage offers a valuable additional vehicle to detect mental health symptoms and potentially accelerate care referral for patients needing care. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, Second Edition)
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Review

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32 pages, 3135 KiB  
Review
Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
by Fatimah Saeed Alhafiz and Abdullah Ahmad Basuhail
COVID 2024, 4(12), 1985-2016; https://doi.org/10.3390/covid4120140 - 16 Dec 2024
Viewed by 754
Abstract
After first appearing in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly, leading to global effects and significant risks to health systems. The virus’s high replication competence in the human lung accelerated the severity of lung pneumonia cases, resulting in a catastrophic death [...] Read more.
After first appearing in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly, leading to global effects and significant risks to health systems. The virus’s high replication competence in the human lung accelerated the severity of lung pneumonia cases, resulting in a catastrophic death rate. Variable observations in the clinical testing of virus-related and patient-related cases across different populations led to ambiguous results. Medical and epidemiological studies on the virus effectively use imaging and scanning devices to help explain the virus’s behavior and its impact on the lungs. Varying equipment resources and a lack of uniformity in medical imaging acquisition led to disorganized and widely dispersed data collection worldwide, while high heterogeneity in datasets caused a poor understanding of the virus and related strains, consequently leading to unstable results that could not be generalized. Hospitals and medical institutions, therefore, urgently need to collaborate to share and extract useful knowledge from these COVID-19 datasets while preserving the privacy of medical records. Researchers are turning to an emerging technology that enhances the reliability and accessibility of information without sharing actual patient data. Federated learning (FL) is a technique that learns distributed data locally, sharing only the weights of each local model to compute a global model, and has the potential to improve the generalization of diagnosis and treatment decisions. This study investigates the applicability of FL for COVID-19 under the impact of data heterogeneity, defining the lung imaging characteristics and identifying the practical constraints of FL in medical fields. It describes the challenges of implementation from a technical perspective, with reference to valuable research directions, and highlights the research challenges that present opportunities for further efforts to overcome the pitfalls of distributed learning performance. The primary objective of this literature review is to provide valuable insights that will aid in the formulation of effective technical strategies to mitigate the impact of data heterogeneity on the generalization of FL results, particularly in light of the ongoing and evolving COVID-19 pandemic. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications for Developing the Diagnosis of COVID-19, Second Edition)
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