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Diagnostics
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Information-Driven Computer-Aided Diagnosis and Decision Support System
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Information-Driven Computer-Aided Diagnosis and Decision Support System

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: 31 August 2025 | Viewed by 5253

Special Issue Editors

Prof. Dr. Wen-Shan Jian

E-Mail Website
Guest Editor
School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan
Interests: Internet of Things; healthcare management; artificial intelligence; data visualization
Special Issues, Collections and Topics in MDPI journals
Dr. Shuo-Chen Chien

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; data visualization; natural language processing; CDSS alert system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence and rapid advancement of information-driven methodologies have significantly transformed the field of medical diagnostics and decision-making. The integration of computer-aided diagnosis and decision support systems in recent years has led to notable improvements in diagnostic accuracy, treatment planning, and patient outcomes. These advancements harness the capabilities of machine learning, artificial intelligence, and data analytics to provide enhanced insights and support to healthcare professionals.

We invite researchers to submit original research articles that delve into the development, implementation, and impact of these advanced systems. Topics of interest include, but are not limited to, machine learning algorithms in healthcare, AI-driven diagnostic tools, predictive modeling, clinical decision support systems, and the integration of healthcare informatics in clinical settings. By contributing to this Special Issue, you will help advance our understanding and the application of cutting-edge technologies in healthcare, ultimately leading to better patient care and clinical efficiency.

Prof. Dr. Wen-Shan Jian
Dr. Shuo-Chen Chien
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 diagnosis
  • decision support systems
  • machine learning in healthcare
  • artificial intelligence in medicine
  • medical data analytics
  • predictive modeling
  • clinical decision-making
  • healthcare informatics
  • diagnostic accuracy
  • patient outcomes

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

Published Papers (4 papers)

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Research

17 pages, 2097 KiB  
Article
A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes
by Chaima Ben Rabah, Aamenah Sattar, Ahmed Ibrahim and Ahmed Serag
Diagnostics 2025, 15(8), 995; https://doi.org/10.3390/diagnostics15080995 - 14 Apr 2025
Viewed by 332
Abstract
Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and [...] Read more.
Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. Methods: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. Results: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. Conclusions: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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14 pages, 3639 KiB  
Article
The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies
by Amjad Shhadeh, Shadi Daoud, Idan Redenski, Daniel Oren, Adeeb Zoabi, Fares Kablan and Samer Srouji
Diagnostics 2025, 15(8), 984; https://doi.org/10.3390/diagnostics15080984 - 12 Apr 2025
Viewed by 207
Abstract
Background/Objectives: Maxillofacial trauma poses significant challenges in emergency medicine, requiring rapid interventions to minimize morbidity and mortality. Traditional segmentation methods are time-consuming and error-prone, particularly in high-pressure settings. Real-time artificial intelligence (AI) segmentation offers a transformative solution to streamline workflows and enhance clinical [...] Read more.
Background/Objectives: Maxillofacial trauma poses significant challenges in emergency medicine, requiring rapid interventions to minimize morbidity and mortality. Traditional segmentation methods are time-consuming and error-prone, particularly in high-pressure settings. Real-time artificial intelligence (AI) segmentation offers a transformative solution to streamline workflows and enhance clinical decision-making. This study evaluated the potential of real-time AI segmentation to improve diagnostic efficiency and support decision-making in maxillofacial trauma emergencies. Methods: This study evaluated 53 trauma patients with moderate to severe maxillofacial injuries treated over 16 months at Galilee Medical Center. AI-assisted segmentation using Materialise Mimics Viewer and Romexis Smart Tool was compared to semi-automated methods in terms of time and accuracy. The clinical impact of AI on diagnosis and treatment planning was also assessed. Results: AI segmentation was significantly faster than semi-automated methods (9.87 vs. 63.38 min) with comparable accuracy (DSC: 0.92–0.93 for AI; 0.95 for semi-automated). AI tools provided rapid 3D visualization of key structures, enabling faster decisions for airway management, fracture assessment, and foreign body localization. Specific trauma cases illustrate the potential of real-time AI segmentation to enhance the efficiency of diagnosis, treatment planning, and overall management of maxillofacial emergencies. The highest clinical benefit was observed in complex cases, such as orbital injuries or combined mandible and midface fractures. Conclusions: Real-time AI segmentation has the potential to enhance efficiency and clinical utility in managing maxillofacial trauma by providing precise, actionable data in time-sensitive scenarios. However, the expertise of oral and maxillofacial surgeons remains critical, with AI serving as a complementary tool to aid, rather than replace, clinical decision-making. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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27 pages, 4989 KiB  
Article
A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction
by Matthew Hodgman, Cristian Minoccheri, Michael Mathis, Emily Wittrup and Kayvan Najarian
Diagnostics 2024, 14(16), 1741; https://doi.org/10.3390/diagnostics14161741 - 10 Aug 2024
Viewed by 1492
Abstract
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction [...] Read more.
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction are not fully understood and little effort exists to use explainable machine learning to learn predictive clinical phenotypes before hospitalization is needed. Methods: We extracted outpatient electronic health record data for 2641 case and 5287 matched-control patients, all without pre-existing cardiac diagnoses, from the Michigan Medicine Health System. We compare six different interpretable, feature extraction approaches, including temporal computational phenotyping, and train seven interpretable machine learning models to predict the onset of first acute myocardial infarction within six months. Results: Using temporal computational phenotypes significantly improved the model performance compared to alternative approaches. The mean cross-validation test set performance exhibited area under the receiver operating characteristic curve values as high as 0.674. The most consistently predictive phenotypes of a future infarction include back pain, cardiometabolic syndrome, family history of cardiovascular diseases, and high blood pressure. Conclusions: Computational phenotyping of longitudinal health records can improve classifier performance and identify predictive clinical concepts. State-of-the-art interpretable machine learning approaches can augment acute myocardial infarction risk assessment and prioritize potential risk factors for further investigation and validation. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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20 pages, 1275 KiB  
Article
Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study
by Haroon Miah, Dimitrios Kollias, Giacinto Luca Pedone, Drew Provan and Frederick Chen
Diagnostics 2024, 14(13), 1352; https://doi.org/10.3390/diagnostics14131352 - 26 Jun 2024
Cited by 1 | Viewed by 2302
Abstract
Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to [...] Read more.
Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting. Various ML models, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from the UK Adult ITP Registry and a general haematology clinic. Two different approaches were investigated: a demographic-unaware and a demographic-aware one. We conduct extensive experiments to evaluate the predictive performance of these models and approaches, as well as their bias. The results revealed that Decision Tree and Random Forest models were both superior and fair, achieving nearly perfect predictive and fairness scores, with platelet count identified as the most significant variable. Models not provided with demographic information performed better in terms of predictive accuracy but showed lower fairness scores, illustrating a trade-off between predictive performance and fairness. Full article
(This article belongs to the Special Issue Information-Driven Computer-Aided Diagnosis and Decision Support System)
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