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Diagnostics
Special Issues
AI and Big Data in Medical Diagnostics
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AI and Big Data in Medical Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 804

Special Issue Editors

Dr. Miguel Mascarenhas

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, 4200-319 Porto, Portugal
2. Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
Interests: inflammatory bowel disease; applied artificial intelligence; capsule endoscopy; neurogastroenterology; coloproctology
Special Issues, Collections and Topics in MDPI journals
Dr. Hélder Cardoso

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
3. World Gastroenterology Organization Porto Training Center, Porto, Portugal
Interests: gastroenterology; hepatology; endoscopy, capsule endoscopy; enteroscopy; applied artificial intelligence; liver cancer; inflammatory bowel diseases
Prof. Dr. Guilherme Macedo

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: gastroenterology; hepatology; liver transplantation; hepatocellular carcinoma; gastrointestinal diseases; biliary tract diseases; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence has subtly and progressively been integrated into our daily life. The applicability of heuristic algorithms revolutionized how we manage extensive databases and AI, providing an adequate framework for systematic analysis and promptly revealing its enormous potential in medical diagnostics.

The excitement of a plausible application of innovative systems in our practice generated overwhelming enthusiasm in our community, and clinicians rapidly dove into a new lexicon, such as convolutional neural network models, deep learning methods, training machines, computer-aided detection systems, etc. Soon, we all realized that cross-pollination research with biomedical engineers, informaticians, and clinicians was more than an episodic drift of our mindset but an indispensable move towards a new, advancing frontier.

In this Special Issue, we aim to showcase the state-of-the-art AI in multiple fields of medical diagnostics, with examples of cutting-edge research that is being carried out in this field.

Dr. Miguel Mascarenhas
Dr. Hélder Cardoso
Prof. Dr. Guilherme Macedo
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

  • artificial intelligence
  • big data
  • diagnosis
  • convolutional neural networks
  • precision medicine

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

Published Papers (2 papers)

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38 pages, 6947 KB  
Article
EfficientNet-B3-Based Automated Deep Learning Framework for Multiclass Endoscopic Bladder Tissue Classification
by A. A. Abd El-Aziz, Mahmood A. Mahmood and Sameh Abd El-Ghany
Diagnostics 2025, 15(19), 2515; https://doi.org/10.3390/diagnostics15192515 - 3 Oct 2025
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Abstract
Background: Bladder cancer (BLCA) is a malignant growth that originates from the urothelial lining of the urinary bladder. Diagnosing BLCA is complex due to the variety of tumor features and its heterogeneous nature, which leads to significant morbidity and mortality. Understanding tumor histopathology [...] Read more.
Background: Bladder cancer (BLCA) is a malignant growth that originates from the urothelial lining of the urinary bladder. Diagnosing BLCA is complex due to the variety of tumor features and its heterogeneous nature, which leads to significant morbidity and mortality. Understanding tumor histopathology is crucial for developing tailored therapies and improving patient outcomes. Objectives: Early diagnosis and treatment are essential to lower the mortality rate associated with bladder cancer. Manual classification of muscular tissues by pathologists is labor-intensive and relies heavily on experience, which can result in interobserver variability due to the similarities in cancerous cell morphology. Traditional methods for analyzing endoscopic images are often time-consuming and resource-intensive, making it difficult to efficiently identify tissue types. Therefore, there is a strong demand for a fully automated and reliable system for classifying smooth muscle images. Methods: This paper proposes a deep learning (DL) technique utilizing the EfficientNet-B3 model and a five-fold cross-validation method to assist in the early detection of BLCA. This model enables timely intervention and improved patient outcomes while streamlining the diagnostic process, ultimately reducing both time and costs for patients. We conducted experiments using the Endoscopic Bladder Tissue Classification (EBTC) dataset for multiclass classification tasks. The dataset was preprocessed using resizing and normalization methods to ensure consistent input. In-depth experiments were carried out utilizing the EBTC dataset, along with ablation studies to evaluate the best hyperparameters. A thorough statistical analysis and comparisons with five leading DL models—ConvNeXtBase, DenseNet-169, MobileNet, ResNet-101, and VGG-16—showed that the proposed model outperformed the others. Conclusions: The EfficientNet-B3 model achieved impressive results: accuracy of 99.03%, specificity of 99.30%, precision of 97.95%, recall of 96.85%, and an F1-score of 97.36%. These findings indicate that the EfficientNet-B3 model demonstrates significant potential in accurately and efficiently diagnosing BLCA. Its high performance and ability to reduce diagnostic time and cost make it a valuable tool for clinicians in the field of oncology and urology. Full article
(This article belongs to the Special Issue AI and Big Data in Medical Diagnostics)
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19 pages, 4647 KB  
Article
Using Machine Learning to Create Prognostic Systems for Primary Prostate Cancer
by Kevin Guan, Andy Guan, Anwar E. Ahmed, Andrew J. Waters, Shyh-Han Tan and Dechang Chen
Diagnostics 2025, 15(19), 2462; https://doi.org/10.3390/diagnostics15192462 - 26 Sep 2025
Viewed by 374
Abstract
Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version [...] Read more.
Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version (2024). This system incorporates five prognostic variables: tumor (T), spread to lymph nodes (N), metastasis (M), prostate-specific antigen (PSA) levels (P), and Grade Group/Gleason score (G). While effective, further refinement of prognostic systems may improve prediction of patient outcomes and support more individualized treatment. Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD), an unsupervised machine learning approach. EACCD involves three steps: calculating initial dissimilarities, performing ensemble learning, and conducting hierarchical clustering. We first developed an EACCD model using the five AJCC variables (T, N, M, P, G). The model was then expanded to include two additional factors, age (A) and race (R). Prostate cancer patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute. Results: The EACCD algorithm effectively stratified patients into distinct prognostic groups, each with well-separated survival curves. The five-variable model achieved a concordance index (C-index) of 0.8293 (95% CI: 0.8245–0.8341), while the seven-variable model, including age and race, improved performance to 0.8504 (95% CI: 0.8461–0.8547). Both outperformed the AJCC TNM system, which had a C-index of 0.7676 (95% CI: 0.7622–0.7731). Conclusions: EACCD provides a refined prognostic framework for primary localized prostate cancer, demonstrating superior accuracy over the AJCC staging system. With further validation in independent cohorts, EACCD could enhance risk stratification and support precision oncology. Full article
(This article belongs to the Special Issue AI and Big Data in Medical Diagnostics)
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