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Diagnostics
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Diagnostic AI and Viral or Bacterial Infection
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Diagnostic AI and Viral or Bacterial Infection

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: 31 October 2024 | Viewed by 9190

Special Issue Editors

Dr. Dilber Uzun Ozsahin

E-Mail Website
Guest Editor
1. Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
2. Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: AI in healthcare; decision making in healthcare; medical imaging; nuclear medicine imaging devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tamer Sanlidag

E-Mail Website
Guest Editor
DESAM Research Institute, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: virology; microbiology; COVID-19; mathematical modelling; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ilker Ozsahin

E-Mail Website
Guest Editor
1. Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
2.Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
Interests: medical imaging; radiology; operational research; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diagnostic artificial intelligence (AI) plays an increasingly important role in identifying viral and bacterial infections since the detection of some of the infectious agents such as tuberculosis and rabies requires experts in the field, is difficult to diagnose, and takes time. Hence, there is a need in the field to accurately and rapidly diagnose infectious disease agents. AI technologies such as machine learning algorithms and computer vision are some example methods that can be readily used to analyse various types of data, such as medical images, clinical data, and molecular information, to assist healthcare professionals in making accurate diagnoses.

Diagnostic AI can be used for viral and bacterial infections such as in medical imaging analysis, including chest X-rays and CT scans; laboratory data analysis: evaluation and interpretation of blood tests, such as inflammatory markers and patterns of bacterial or viral infections; molecular diagnostics: results from polymerase chain reaction (PCR) or other molecular tests to detect specific pathogens; and epidemiological data: assessing the prevalence of specific infections in a given region, which can influence the diagnostic process.

AI can assist healthcare professionals in keeping up to date with the latest research and guidelines for diagnosing and treating infections. For this Special Issue, in the interest of improving the diagnosis of viral and bacterial infection, we invite authors to submit high-quality research on novel approaches based on AI methods and/or their applications.

Dr. Dilber Uzun Ozsahin
Prof. Dr. Tamer Sanlidag
Dr. Ilker Ozsahin
Guest Editors

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.

Published Papers (5 papers)

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Research

49 pages, 34748 KiB  
Article
Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework
by Arun Kumar Dubey, Gian Luca Chabert, Alessandro Carriero, Alessio Pasche, Pietro S. C. Danna, Sushant Agarwal, Lopamudra Mohanty, Nillmani, Neeraj Sharma, Sarita Yadav, Achin Jain, Ashish Kumar, Mannudeep K. Kalra, David W. Sobel, John R. Laird, Inder M. Singh, Narpinder Singh, George Tsoulfas, Mostafa M. Fouda, Azra Alizad, George D. Kitas, Narendra N. Khanna, Klaudija Viskovic, Melita Kukuljan, Mustafa Al-Maini, Ayman El-Baz, Luca Saba and Jasjit S. Suriadd Show full author list remove Hide full author list
Diagnostics 2023, 13(11), 1954; https://doi.org/10.3390/diagnostics13111954 - 02 Jun 2023
Cited by 13 | Viewed by 2343
Abstract
Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are [...] Read more.
Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses. Full article
(This article belongs to the Special Issue Diagnostic AI and Viral or Bacterial Infection)
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22 pages, 1499 KiB  
Article
Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests
by Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane, Farid Kadri and Ying Sun
Diagnostics 2023, 13(8), 1466; https://doi.org/10.3390/diagnostics13081466 - 18 Apr 2023
Cited by 2 | Viewed by 1308
Abstract
This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from [...] Read more.
This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method’s performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data. Full article
(This article belongs to the Special Issue Diagnostic AI and Viral or Bacterial Infection)
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17 pages, 415 KiB  
Article
On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events
by Huda M. Alshanbari, Hasnain Iftikhar, Faridoon Khan, Moeeba Rind, Zubair Ahmad and Abd Al-Aziz Hosni El-Bagoury
Diagnostics 2023, 13(7), 1310; https://doi.org/10.3390/diagnostics13071310 - 31 Mar 2023
Cited by 8 | Viewed by 1228
Abstract
The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases [...] Read more.
The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan’s daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model. Full article
(This article belongs to the Special Issue Diagnostic AI and Viral or Bacterial Infection)
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21 pages, 10077 KiB  
Article
Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models
by Saleh Almuayqil, Sameh Abd El-Ghany and Abdulaziz Shehab
Diagnostics 2023, 13(7), 1268; https://doi.org/10.3390/diagnostics13071268 - 28 Mar 2023
Cited by 1 | Viewed by 1183
Abstract
In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned [...] Read more.
In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time. Full article
(This article belongs to the Special Issue Diagnostic AI and Viral or Bacterial Infection)
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14 pages, 3834 KiB  
Article
COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach
by Dilber Uzun Ozsahin, Efe Precious Onakpojeruo, Basil Bartholomew Duwa, Abdullahi Garba Usman, Sani Isah Abba and Berna Uzun
Diagnostics 2023, 13(7), 1264; https://doi.org/10.3390/diagnostics13071264 - 27 Mar 2023
Cited by 4 | Viewed by 2046
Abstract
The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or [...] Read more.
The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases. Full article
(This article belongs to the Special Issue Diagnostic AI and Viral or Bacterial Infection)
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