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Diagnostics
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Artificial Intelligence-Based Clinical Decision-Making Applications for Disease Diagnosis
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Artificial Intelligence-Based Clinical Decision-Making Applications for Disease Diagnosis

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 3577

Special Issue Editor

Prof. Dr. Cemil Çolak

E-Mail Website
Guest Editor
Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya, Turkey
Interests: medicine; fundamental medical sciences; biostatistics and medical informatics; epidemiology; information security and reliability; computer vision; bioinformatics; artificial intelligence; computer learning and pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), which imitates human intelligence by modeling the functional features of the human brain, has long been used for various purposes in medicine and other disciplines. AI-based clinical-decision-making applications provide practical assistance, especially in medical interventions, helping physicians to collect, analyze and interpret extensive dataset(s) for diagnosing diseases in daily practice.

This Special Issue welcomes scientific studies on AI-based clinical-decision-making applications for diagnosing diseases from a clinical perspective.

Potential topics include, but are not limited to:

  • Developing AI-based clinical-decision-making applications;
  • Ensemble learning applications for disease diagnosis;
  • IntegratingAI systems into clinical decisions;
  • Adopted electronic health record systems;
  • Deep-learning-based clinical decision support systems;
  • Meta-learning-based computer-assisted applications for big biomedical data;
  • Explainable artificial intelligence for medical decision making in bioinformatics.

Prof. Dr. Cemil Çolak
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
  • clinical decision making
  • computer-assisted technologies
  • clinical studies
  • meta learning
  • ensemble learning
  • scientific guidelines
  • deep learning

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Published Papers (2 papers)

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Research

21 pages, 3722 KiB  
Article
Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms
by Zelal Shearah, Zahid Ullah and Bahjat Fakieh
Diagnostics 2023, 13(20), 3204; https://doi.org/10.3390/diagnostics13203204 - 13 Oct 2023
Viewed by 1617
Abstract
Children’s health is one of the most significant fields in medicine. Most diseases that result in children’s death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research [...] Read more.
Children’s health is one of the most significant fields in medicine. Most diseases that result in children’s death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children’s urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child’s medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Clinical Decision-Making Applications for Disease Diagnosis)
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32 pages, 9510 KiB  
Article
MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model
by Ayesha Ahoor, Fahim Arif, Muhammad Zaheer Sajid, Imran Qureshi, Fakhar Abbas, Sohail Jabbar and Qaisar Abbas
Diagnostics 2023, 13(20), 3195; https://doi.org/10.3390/diagnostics13203195 - 12 Oct 2023
Cited by 2 | Viewed by 1528
Abstract
The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control [...] Read more.
The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Clinical Decision-Making Applications for Disease Diagnosis)
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