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Diagnostics
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Artificial Intelligence in Biomedical Diagnostics and Analysis 2024
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Artificial Intelligence in Biomedical Diagnostics and Analysis 2024

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: 30 June 2025 | Viewed by 2427

Special Issue Editors

Dr. Prabal Datta Barua

E-Mail Website
Guest Editor
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: machine learning; artificial intelligence; deep learning; neural networks; data science; information and communication management
Special Issues, Collections and Topics in MDPI journals
Dr. Turker Tuncer

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Interests: image processing; signal processing; data hiding; feature engineering; visual secret sharing
Special Issues, Collections and Topics in MDPI journals
Dr. Sengul Dogan

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
Interests: feature engineering; machine learning; biomedical image and signal processing; pattern recognition; computer forensics; mobile forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has allowed us to propose algorithms/methods that make many tasks easier, and smart assistants have been proposed using artificial intelligence. These smart assistants, now being used in daily life, are of great importance in shortening processes. As a result, the quality of services has increased, especially with smart systems used in the healthcare field.

In this Special Issue, we plan to publish articles on next-generation machine learning methods. In particular, machine learning models are expected to be proposed using signals such as electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (ECG), heart rate (HR) signals, computed tomography (CT), magnetic resonance (MR), X-rays and other medical images or videos. Additionally, explainable artificial intelligence (XAI) explains how machine learning methods perform classification. We hope to publish smart health applications that incorporate XAI-based next-generation methods.

Artificial intelligence applications in healthcare are an important research area, and these models/architectures/networks have also been used in precision medicine applications. With the Internet of Medical Things (IoMT), precision medical data can be accessed instantly, and information can be obtained from these data via machine learning methods.

Proteins and genomes are crucial to bioinformatics. Using genomic data, disorders and their associations can be identified. Therefore, we are interested in AI-based biomedical informatics methods, since biomedical informatics is crucial to understand the causes of disorders.

We have proposed a new Special Issue to contribute to the study area of healthcare with artificial intelligence and publish high-quality research articles. We look forward to receiving your submissions on feature engineering, deep learning and XAI-based models, as well as their uncertainty and implementation.

Dr. Prabal Datta Barua
Dr. Turker Tuncer
Dr. Sengul Dogan
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

  • medical signal and image processing
  • Internet of Medical Things (IoMT)
  • bioinformatics
  • explainable artificial intelligence
  • machine learning
  • uncertainty

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Published Papers (4 papers)

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26 pages, 5539 KiB  
Article
A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer
by Elif Yusufoğlu, Hüseyin Fırat, Hüseyin Üzen, Salih Taha Alperen Özçelik, İpek Balıkçı Çiçek, Abdulkadir Şengür, Orhan Atila and Numan Halit Guldemir
Diagnostics 2024, 14(24), 2836; https://doi.org/10.3390/diagnostics14242836 - 17 Dec 2024
Viewed by 362
Abstract
Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies [...] Read more.
Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method’s diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2024)
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22 pages, 4940 KiB  
Article
Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network
by Salih Taha Alperen Özçelik, Hüseyin Üzen, Abdulkadir Şengür, Hüseyin Fırat, Muammer Türkoğlu, Adalet Çelebi, Sema Gül and Nebras M. Sobahi
Diagnostics 2024, 14(23), 2719; https://doi.org/10.3390/diagnostics14232719 - 3 Dec 2024
Viewed by 660
Abstract
Background: Dental disorders are one of the most important health problems, affecting billions of people all over the world. Early diagnosis is important for effective treatment planning. Precise dental disease segmentation requires reliable tooth numbering, which may be prone to errors if performed [...] Read more.
Background: Dental disorders are one of the most important health problems, affecting billions of people all over the world. Early diagnosis is important for effective treatment planning. Precise dental disease segmentation requires reliable tooth numbering, which may be prone to errors if performed manually. These steps can be automated using artificial intelligence, which may provide fast and accurate results. Among the AI methodologies, deep learning has recently shown excellent performance in dental image processing, allowing effective tooth segmentation and numbering. Methods: This paper proposes the Squeeze and Excitation Inception Block-based Encoder–Decoder (SE-IB-ED) network for teeth segmentation in panoramic X-ray images. It combines the InceptionV3 model for encoding with a custom decoder for feature integration and segmentation, using pointwise convolution and an attention mechanism. A dataset of 313 panoramic radiographs from private clinics was annotated using the Fédération Dentaire Internationale (FDI) system. PSPL and SAM augmented the annotation precision and effectiveness, with SAM automating teeth labeling and subsequently applying manual corrections. Results: The proposed SE-IB-ED network was trained and tested using 80% training and 20% testing of the dataset, respectively. Data augmentation techniques were employed during training. It outperformed the state-of-the-art models with a very high F1-score of 92.65%, mIoU of 86.38%, and 92.84% in terms of accuracy, precision of 92.49%, and recall of 99.92% in the segmentation of teeth. Conclusions: According to the results obtained, the proposed method has great potential for the accurate segmentation of all teeth regions and backgrounds in panoramic X-ray images. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2024)
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21 pages, 4809 KiB  
Article
Cardioish: Lead-Based Feature Extraction for ECG Signals
by Turker Tuncer, Abdul Hafeez Baig, Emrah Aydemir, Tarik Kivrak, Ilknur Tuncer, Gulay Tasci and Sengul Dogan
Diagnostics 2024, 14(23), 2712; https://doi.org/10.3390/diagnostics14232712 - 30 Nov 2024
Viewed by 482
Abstract
Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and [...] Read more.
Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2024)
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35 pages, 528 KiB  
Systematic Review
Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review
by Kubra Demir, Ozlem Sokmen, Isil Karabey Aksakalli and Kubra Torenek-Agirman
Diagnostics 2024, 14(23), 2768; https://doi.org/10.3390/diagnostics14232768 - 9 Dec 2024
Viewed by 567
Abstract
Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. [...] Read more.
Background/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2024)
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