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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 December 2023) | Viewed by 10496

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Dr. Shaker El-Sappagh

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Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
Interests: explainable AI; deep learning; machine learning; trustworthy AI; medical informatics; uncertainty quantification
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Special Issue Information

Dear Colleagues,

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. Despite the advancements in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. In the healthcare system, there are instances where AI has done marvels in the diagnosis of various health conditions and the interpretation of complex medical disorders. This Special Issue aims to identify potential applications of artificial intelligence and machine learning in the field of infectious diseases.

Dr. Shaker El-Sappagh
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
machine learning
infectious diseases
diagnostics

Published Papers (6 papers)

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Research

16 pages, 2552 KiB

Open AccessArticle

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

by William Hoyos, Kenia Hoyos and Rander Ruíz

Diagnostics 2024, 14(5), 533; https://doi.org/10.3390/diagnostics14050533 - 02 Mar 2024

Viewed by 617

Abstract

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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14 pages, 2102 KiB

Open AccessArticle

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients

by Rita Murri, Giulia De Angelis, Laura Antenucci, Barbara Fiori, Riccardo Rinaldi, Massimo Fantoni, Andrea Damiani, Stefano Patarnello, Maurizio Sanguinetti, Vincenzo Valentini, Brunella Posteraro and Carlotta Masciocchi

Diagnostics 2024, 14(4), 445; https://doi.org/10.3390/diagnostics14040445 - 17 Feb 2024

Viewed by 597

Abstract

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016–2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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23 pages, 1218 KiB

Open AccessArticle

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

by Abdulaziz AlMohimeed, Hager Saleh, Nora El-Rashidy, Redhwan M. A. Saad, Shaker El-Sappagh and Sherif Mostafa

Diagnostics 2023, 13(11), 1968; https://doi.org/10.3390/diagnostics13111968 - 05 Jun 2023

Cited by 6 | Viewed by 1484

Abstract

The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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18 pages, 425 KiB

Open AccessArticle

Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique

by Hasnain Iftikhar, Murad Khan, Mohammed Saad Khan and Mehak Khan

Diagnostics 2023, 13(11), 1923; https://doi.org/10.3390/diagnostics13111923 - 31 May 2023

Cited by 7 | Viewed by 1115

Abstract

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology’s performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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17 pages, 759 KiB

Open AccessArticle

COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection

by Ibtihal A. L. Alablani and Mohammed J. F. Alenazi

Diagnostics 2023, 13(10), 1675; https://doi.org/10.3390/diagnostics13101675 - 09 May 2023

Cited by 1 | Viewed by 1352

Abstract

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population’s health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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14 pages, 1098 KiB

Open AccessArticle

Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework

by Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Berna Uzun, Basil Duwa and Ilker Ozsahin

Diagnostics 2023, 13(2), 292; https://doi.org/10.3390/diagnostics13020292 - 12 Jan 2023

Cited by 18 | Viewed by 4321

Abstract

Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state-of-the-art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Infectious Diseases)

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