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Diagnostics
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Using Artificial Intelligence for the Early Detection of Pneumonia and...
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Using Artificial Intelligence for the Early Detection of Pneumonia and Its Further Management

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 (30 September 2023) | Viewed by 7467

Special Issue Editor

Dr. Kuangming Liao

E-Mail Website
Guest Editor
Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan 72263, Taiwan
Interests: palliative care; chronic obstructive pulmonary disease; healthcare utilization

Special Issue Information

Dear Colleagues,

Pneumonia is one of the most important infectious diseases and is caused by a number of infectious pathogens, including viruses, bacteria and fungi. Pneumonia may have a devastating effect on morbidities and mortality in the elderly population. How best to pursue diagnosis and treatment is an important issue. We aim to encourage researchers to investigate the efficiency of the diagnostic processes for pneumonia and the early identification of the disease. Therefore, the early detection and treatment of pneumonia may improve the prognosis.

Previous studies have shown that Artificial Intelligence can help physicians to analyze and detect disease at an early stage. Artificial intelligence increases sensitivity and specificity to correctly identify patients who have the disease. Machine learning models can also integrate with the existing hospital information system to provide physicians with a useful decision-making reference. It may be useful to set prediction models with patients' characteristics, laboratory data, and comorbidities for the early detection of pneumonia.

Dr. Kuangming Liao
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

  • pneumonia
  • artificial intelligence
  • machine learning
  • diagnostic tool
  • treatment

Published Papers (4 papers)

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Research

15 pages, 979 KiB  
Article
Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks
by Umut Kaya, Atınç Yılmaz and Sinan Aşar
Diagnostics 2023, 13(12), 2023; https://doi.org/10.3390/diagnostics13122023 - 10 Jun 2023
Cited by 2 | Viewed by 1227
Abstract
The early diagnosis of sepsis reduces the risk of the patient’s death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In [...] Read more.
The early diagnosis of sepsis reduces the risk of the patient’s death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for the Early Detection of Pneumonia and Its Further Management)
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13 pages, 12821 KiB  
Article
A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study
by Lizhen Duan, Longjiang Zhang, Guangming Lu, Lili Guo, Shaofeng Duan and Changsheng Zhou
Diagnostics 2023, 13(8), 1479; https://doi.org/10.3390/diagnostics13081479 - 19 Apr 2023
Viewed by 1184
Abstract
This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed [...] Read more.
This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for the Early Detection of Pneumonia and Its Further Management)
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25 pages, 6387 KiB  
Article
Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features
by Ibrahim Abdulrab Ahmed, Ebrahim Mohammed Senan, Hamzeh Salameh Ahmad Shatnawi, Ziad Mohammad Alkhraisha and Mamoun Mohammad Ali Al-Azzam
Diagnostics 2023, 13(4), 814; https://doi.org/10.3390/diagnostics13040814 - 20 Feb 2023
Cited by 12 | Viewed by 2260
Abstract
An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and [...] Read more.
An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for the Early Detection of Pneumonia and Its Further Management)
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13 pages, 2841 KiB  
Article
Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs
by Pranav Ajmera, Prashant Onkar, Sanjay Desai, Richa Pant, Jitesh Seth, Tanveer Gupte, Viraj Kulkarni, Amit Kharat, Nandini Passi, Sanjay Khaladkar and V. M. Kulkarni
Diagnostics 2023, 13(3), 557; https://doi.org/10.3390/diagnostics13030557 - 02 Feb 2023
Cited by 3 | Viewed by 2312
Abstract
Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective [...] Read more.
Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p < 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for the Early Detection of Pneumonia and Its Further Management)
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