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Diagnostics
Special Issues
Artificial Intelligence-Driven Radiomics in Medical Diagnosis
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Artificial Intelligence-Driven Radiomics in Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 244

Special Issue Editor

Dr. Shara WY Lee

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
Interests: clinical trials; immersive technology in clinical skills education; clinical applications of photobiomodulation among cancer patients; non-invasive haemodynamic monitoring in transfusion medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI) and medical imaging signifies a paradigm shift in healthcare diagnostics. Traditional diagnostic methodologies, reliant on the human eye and expertise, possess inherent limitations such as inter-observer variability. AI, especially deep learning architectures like convolutional neural networks (CNNs), offers a remedy. These algorithms are adept at pattern recognition, extracting intricate features from images often imperceptible to clinicians. As a result, AI-driven models have shown remarkable proficiency in tasks ranging from tumour detection in radiographs to retinal disease classification in ophthalmic images. Moreover, AI's potential extends beyond mere diagnosis. Predictive modelling, image reconstruction, and workflow optimisation are facets undergoing rapid transformation under AI's influence. However, the marriage of AI and medical imaging is not without challenges: data privacy, algorithmic transparency, and clinical integration pose pertinent questions. This Special Issue delves into these advancements and hurdles, providing a holistic perspective on the current state and future trajectory of AI in the medical imaging domain. It underscores the pivotal role AI is positioned to play in shaping the next frontier of diagnostic medicine.

Dr. Shara WY Lee
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

  • deep learning
  • convolutional neural networks (CNNs)
  • medical imaging
  • diagnostic algorithms
  • image reconstruction
  • pattern recognition
  • predictive modelling
  • clinical integration
  • tumour identification
  • algorithmic transparency
  • image segmentation
  • diagnostic accuracy

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Published Papers (1 paper)

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Research

15 pages, 1361 KiB  
Article
Radiomics with Clinical Data and [18F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study
by Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha and Ben R. Saleem
Diagnostics 2025, 15(15), 1944; https://doi.org/10.3390/diagnostics15151944 (registering DOI) - 2 Aug 2025
Abstract
Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [18F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on [...] Read more.
Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [18F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [18F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)
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