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Diagnostics
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Computer-Assisted Functional Diagnostics
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Computer-Assisted Functional Diagnostics

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 April 2024) | Viewed by 11737

Special Issue Editors

Prof. Dr. Ayman El-Baz

E-Mail Website
Guest Editor
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: biosignaling; bioimaging modeling and computer-assisted functional diagnostic diagnosis systems, including those using CT, MRI, EMG, ECG, EEG, and other physiological signals
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Mahmoud

E-Mail Website
Guest Editor
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: image processing; artificial intelligence; bio-imaging modeling; non-invasive computer-assisted diagnosis systems; computer vision
Dr. Ahmed Shalaby

E-Mail Website
Guest Editor
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: image processing; artificial intelligence; bio-imaging modeling; non-invasive computer-assisted diagnosis systems; computer vision
Prof. Dr. Mohammed Ghazal

E-Mail Website
Guest Editor
College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Interests: bioimaging; image/video processing; smart systems; machine learning; sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) algorithms progress rapidly, we are expected to have, in the near future, machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. Currently, diagnosing diseases is mainly dependent on humans as the patients’ data (scans, lab tests, etc.) are primarily interpreted by physicians, who make the final diagnosis. This process can be subjective and time consuming, but thanks to AI, these limitations will be able to be handled. This Special Issue will present the state-of-the-art AI techniques that can be used to develop non-invasive computer aided diagnostics (CAD) systems for the early detection of severe diseases and disorders. The ultimate goal is to comprehensively focus on the different AI algorithms that can be used to build CAD systems, while highlighting the novel applications.

The Special Issue subtopics include but are not limited to the following:

  • Developing non-invasive computer-aided diagnosis systems;
  • Deep learning for medical imaging;
  • Medical image segmentation;
  • Early detection of cancer;
  • Early detection of neurological disorders;
  • Early detection of cardiovascular diseases.

Prof. Dr. Ayman El-Baz
Dr. Ali Mahmoud
Dr. Ahmed Shalaby
Prof. Dr. Mohammed Ghazal
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.

Keywords

  • computer-aided diagnostics (CAD)
  • non-invasive
  • artificial intelligence
  • machine learning
  • early detection

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

13 pages, 1693 KiB  
Article
Diagnostic Algorithm Based on Machine Learning to Predict Complicated Appendicitis in Children Using CT, Laboratory, and Clinical Features
by Jieun Byun, Seongkeun Park and Sook Min Hwang
Diagnostics 2023, 13(5), 923; https://doi.org/10.3390/diagnostics13050923 - 1 Mar 2023
Cited by 5 | Viewed by 2462
Abstract
To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features. Methods: This retrospective study included 315 children (<18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A [...] Read more.
To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features. Methods: This retrospective study included 315 children (<18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A decision tree algorithm was used to identify important features associated with the condition and to develop a diagnostic algorithm for predicting complicated appendicitis, including CT and clinical findings in the development cohort (n = 198). Complicated appendicitis was defined as gangrenous or perforated appendicitis. The diagnostic algorithm was validated using a temporal cohort (n = 117). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) from the receiver operating characteristic curve analysis were calculated to evaluate the diagnostic performance of the algorithm. Results: All patients with periappendiceal abscesses, periappendiceal inflammatory masses, and free air on CT were diagnosed with complicated appendicitis. In addition, intraluminal air, transverse diameter of the appendix, and ascites were identified as important CT findings for predicting complicated appendicitis. C-reactive protein (CRP) level, white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and body temperature also showed important associations with complicated appendicitis. The AUC, sensitivity, and specificity of the diagnostic algorithm comprising features were 0.91 (95% CI, 0.86–0.95), 91.8% (84.5–96.4), and 90.0% (82.4–95.1) in the development cohort, and 0.7 (0.63–0.84), 85.9% (75.0–93.4), and 58.5% (44.1–71.9) in test cohort, respectively. Conclusion: We propose a diagnostic algorithm based on a decision tree model using CT and clinical findings. This algorithm can be used to differentiate between complicated and noncomplicated appendicitis and to provide an appropriate treatment plan for children with acute appendicitis. Full article
(This article belongs to the Special Issue Computer-Assisted Functional Diagnostics)
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22 pages, 5242 KiB  
Article
Diagnosis of Peritoneal Carcinomatosis of Colorectal Origin Based on an Innovative Fuzzy Logic Approach
by Valentin Bejan, Marius Pîslaru and Viorel Scripcariu
Diagnostics 2022, 12(5), 1285; https://doi.org/10.3390/diagnostics12051285 - 21 May 2022
Cited by 4 | Viewed by 1884
Abstract
Colorectal cancer represents one of the most important causes worldwide of cancer related morbidity and mortality. One of the complications which can occur during cancer progression, is peritoneal carcinomatosis. In the majority of cases, it is diagnosed in late stages due to the [...] Read more.
Colorectal cancer represents one of the most important causes worldwide of cancer related morbidity and mortality. One of the complications which can occur during cancer progression, is peritoneal carcinomatosis. In the majority of cases, it is diagnosed in late stages due to the lack of diagnostic tools capable of revealing the early-stage peritoneal burden. Therefore, still associates with poor prognosis and quality of life, despite recent therapeutic advances. The aim of the study was to develop a fuzzy logic approach to assess the probability of peritoneal carcinomatosis presence using routine blood test parameters as input data. The patient data was acquired retrospective from patients diagnosed between 2010–2021. The developed model focuses on the specific quantitative alteration of these parameters in the presence of peritoneal carcinomatosis, which is an innovative approach as regards the literature in the field and validates the feasibility of using a fuzzy logic approach in the noninvasive diagnosis of peritoneal carcinomatosis. Full article
(This article belongs to the Special Issue Computer-Assisted Functional Diagnostics)
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16 pages, 10996 KiB  
Article
The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients
by Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy and Ayman El-Baz
Diagnostics 2022, 12(3), 696; https://doi.org/10.3390/diagnostics12030696 - 12 Mar 2022
Cited by 15 | Viewed by 2742
Abstract
Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to [...] Read more.
Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest. Full article
(This article belongs to the Special Issue Computer-Assisted Functional Diagnostics)
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25 pages, 16910 KiB  
Article
Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier
by Abdulqader M. Almars, Majed Alwateer, Mohammed Qaraad, Souad Amjad, Hanaa Fathi, Ayda K. Kelany, Nazar K. Hussein and Mostafa Elhosseini
Diagnostics 2021, 11(10), 1936; https://doi.org/10.3390/diagnostics11101936 - 19 Oct 2021
Cited by 4 | Viewed by 2601
Abstract
The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting [...] Read more.
The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91±0.12, the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81± 0.24. By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF (0.97±0.08) achieve equivalent and higher classification accuracy than NB (0.91±0.12). The findings of relevant biomedical studies confirm the findings of the selected genes. Full article
(This article belongs to the Special Issue Computer-Assisted Functional Diagnostics)
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