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Diagnostics
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AI-Based COVID-19 Detection
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AI-Based COVID-19 Detection

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 10007

Special Issue Editor

Dr. Mostafa Fouda

E-Mail Website1 Website2
Guest Editor
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Interests: cybersecurity; artificial intelligence (AI); internet of things (IoT); smart grids; 5G/6G networks; vehicular networks; communication networks; image processing; signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is intended to present features, and scholarly papers that address some of the diverse array of topics related to the use of artificial intelligence (AI) in the detection of COVID-19 through chest X-ray scans, lung computed tomography (CT) scans, or magnetic resonance imaging (MRI) images. These topics include but are not limited to, AI for COVID-19 Detection, AI for COVID-19 Lesion Segmentation, and Explainable AI for COVID-19 Detection. We invite scientists and researchers to submit papers for this important Special Issue, “AI-Based COVID-19 Detection”.

  • AI for COVID-19 Detection
  • AI for COVID-19 Lesion Segmentation
  • Explainable AI for COVID-19 Detection

Dr. Mostafa Fouda
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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 (AI)
  • explainable AI
  • neural networks
  • COVID-19 detection
  • COVID-19 lesion segmentation

Published Papers (4 papers)

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Research

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12 pages, 853 KiB  
Article
Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients
by Hyun Joo Shin, Min Hyung Kim, Nak-Hoon Son, Kyunghwa Han, Eun-Kyung Kim, Yong Chan Kim, Yoon Soo Park, Eun Hye Lee and Taeyoung Kyong
Diagnostics 2023, 13(12), 2090; https://doi.org/10.3390/diagnostics13122090 - 16 Jun 2023
Viewed by 1180
Abstract
This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A [...] Read more.
This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate −0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005–1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients. Full article
(This article belongs to the Special Issue AI-Based COVID-19 Detection)
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12 pages, 719 KiB  
Article
COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach
by Areej Fatima, Tariq Shahzad, Sagheer Abbas, Abdur Rehman, Yousaf Saeed, Meshal Alharbi, Muhammad Adnan Khan and Khmaies Ouahada
Diagnostics 2023, 13(2), 270; https://doi.org/10.3390/diagnostics13020270 - 11 Jan 2023
Cited by 1 | Viewed by 1385
Abstract
COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading [...] Read more.
COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people’s symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. Full article
(This article belongs to the Special Issue AI-Based COVID-19 Detection)
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36 pages, 9484 KiB  
Communication
Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity
by Madhusree Kuanr, Puspanjali Mohapatra, Sanchi Mittal, Mahesh Maindarkar, Mostafa M. Fouda, Luca Saba, Sanjay Saxena and Jasjit S. Suri
Diagnostics 2022, 12(11), 2700; https://doi.org/10.3390/diagnostics12112700 - 05 Nov 2022
Cited by 9 | Viewed by 2346
Abstract
Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital [...] Read more.
Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell–Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings. Full article
(This article belongs to the Special Issue AI-Based COVID-19 Detection)
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Review

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14 pages, 1212 KiB  
Review
Cardiac Involvement in Children Affected by COVID-19: Clinical Features and Diagnosis
by Elena Vasichkina, Daria Alekseeva, Vadim Karev, Ekaterina Podyacheva, Igor Kudryavtsev, Anzhela Glushkova, Anastasia Y. Starshinova, Dmitry Kudlay and Anna Starshinova
Diagnostics 2023, 13(1), 120; https://doi.org/10.3390/diagnostics13010120 - 30 Dec 2022
Cited by 7 | Viewed by 3684
Abstract
COVID-19 (Coronavirus disease 2019) in children is usually mild. However, multiple organ disorders associated with SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus 2) have been detected with poor respiratory symptoms. Cardiac changes are noted in 17% to 75% of cases, [...] Read more.
COVID-19 (Coronavirus disease 2019) in children is usually mild. However, multiple organ disorders associated with SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus 2) have been detected with poor respiratory symptoms. Cardiac changes are noted in 17% to 75% of cases, which are associated with diagnostic difficulties in high-risk groups for the development of complications that are associated with myocardial damage by the SARS-CoV-2 virus. The objective of this review is to identify the most significant symptoms of cardiac involvement affected by COVID-19, which require in-depth examination. The authors analyzed publications from December 2019 to the October 2022, which were published in accessible local and international databases. According to the analysis data, the main sign of myocardial involvement was increasing as cardiomarkers in the patient’s blood, in particular troponin I or troponin T. Many authors noted that the increased level of CRP (C-reactive protein) and NT-proBNP, which are accompanied by changes in the ECG (electrocardiogram) and EchoCG (echocardiography), as a rule, were nonspecific. However, the identified cardiac functional dysfunctions affected by SARS-CoV-2, required an cardiac MRI. The lack of timely diagnosis of myocardial involvements, especially in children at high risk for the development of complications associated with SARS-CoV-2 myocardial injury, can lead to death. The direct damage of the structural elements of myocardial blood vessels in patients with severe hypoxic changes resulted from respiratory failure caused by SARS-CoV-2 lung damage, with the development of severe acute diffuse alveolar damage and cell-mediated immune response and myocardial involvement affected by SARS-CoV-2 damage. In this article, the authors introduce a clinical case of a child who dead from inflammatory myocardities with COVID-19 in a background of congenital heart disease and T-cell immunodeficiency. Full article
(This article belongs to the Special Issue AI-Based COVID-19 Detection)
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