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Artificial Intelligence in Medical Radiation Science, Radiology and Radiation Oncology
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Artificial Intelligence in Medical Radiation Science, Radiology and Radiation Oncology

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1414

Special Issue Editor

Dr. Curtise K. C. Ng

E-Mail Website
Guest Editor
Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
Interests: artificial intelligence; deep learning; imaging informatics; machine learning; medical education; medical radiation science; radiation oncology; radiation protection; radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, artificial intelligence (AI) has become a popular research topic in medicine, aiming at improving health care service quality and efficiency for individuals and populations. Simultaneously, the roles of the medical specialties, medical radiation science, radiology and radiation oncology have also become more important, but their existing workforces appear unable to match the increased demand. Hence, various AI-based solutions developed by multi-disciplinary teams of computer scientists and engineers, radiologists, radiographers, radiation oncologists, radiation therapists, medical physicists and dosimetrists have emerged to address this issue with reported performances similar to or even better than gold standards. Examples of these solutions include automatic image segmentation, computer-aided diagnosis, automated structured reporting, radiation dose optimization, clinical decision support and image quality enhancement. However, these promising results were mainly based on retrospective studies and/or those with relatively small sample sizes. Also, ethical issues in AI in medical radiation science, radiology and radiation oncology, e.g., automation bias, commission errors, responsibility for medical errors, etc., have been raised by influential bodies.

This Special Issue aims to collect medical radiation science, radiology or radiation oncology AI research findings and/or literature review papers, focusing on artificial intelligence in medical radiation science, radiology and radiation oncology that is advancing AI research in these fields.

Dr. Curtise K. C. Ng
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. Multimodal Technologies and Interaction is an international peer-reviewed open access monthly 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 1600 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
  • deep learning
  • ethics
  • machine learning
  • medical imaging
  • medical radiation science
  • nuclear medicine
  • radiation oncology
  • radiation therapy
  • radiology

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Published Papers (2 papers)

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12 pages, 683 KiB  
Article
Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach
by S. J. Pawan, Matthew Muellner, Xiaomeng Lei, Mihir Desai, Bino Varghese, Vinay Duddalwar and Steven Y. Cen
Multimodal Technol. Interact. 2025, 9(5), 49; https://doi.org/10.3390/mti9050049 - 21 May 2025
Abstract
Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights. However, its effectiveness depends on the selection of dimensionality reduction techniques, clustering methods, and hyperparameter [...] Read more.
Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights. However, its effectiveness depends on the selection of dimensionality reduction techniques, clustering methods, and hyperparameter optimization, an area with limited exploration in the literature. We present a novel bootstrapping-based hyperparameter search approach to optimize clustering efficacy, treating dimensionality reduction and clustering as a connected process chain. The hyperparameter search was guided by the Adjusted Rand Index (ARI) and Davies–Bouldin Index (DBI) within a bootstrapping framework of 100 iterations. The cluster assignments were generated through 10-fold cross-validation, and a grid search strategy was used to explore hyperparameter combinations. We evaluated ten unsupervised learning pipelines using both simulation studies and real-world radiomics data derived from multiphase CT images of renal cell carcinoma. In simulations, we found that Non-negative Matrix Factorization (NMF) and Spectral Clustering outperformed the traditional Principal Component Analysis (PCA)-based approach. The best-performing pipeline (NMF followed by K-means clustering) successfully identified all three simulated clusters, achieving a Cramér’s V of 0.9. The simulation also established a reference framework for understanding the concordance patterns among different pipelines under varying strengths of clustering effects. High concordance reflects strong clustering. In the real-world data application, we observed a moderate clustering effect, which aligned with the weak associations to clinical outcomes, as indicated by the highest AUROC of 0.63. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Radiation Science, Radiology and Radiation Oncology)
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13 pages, 1830 KiB  
Article
Application of 9-Channel Pseudo-Color Maps in Deep Learning for Intracranial Hemorrhage Detection
by Shimpei Sato, Daisuke Oura and Hiroyuki Sugimori
Multimodal Technol. Interact. 2025, 9(2), 17; https://doi.org/10.3390/mti9020017 - 14 Feb 2025
Viewed by 721
Abstract
[Background] In computed tomography (CT) for intracranial hemorrhage (ICH), the various window settings and the continuity of slices are critical factors for accurate diagnosis. However, traditional convolutional neural networks typically accept only single-slice images. Since ICH lesions often extend across multiple slices, using [...] Read more.
[Background] In computed tomography (CT) for intracranial hemorrhage (ICH), the various window settings and the continuity of slices are critical factors for accurate diagnosis. However, traditional convolutional neural networks typically accept only single-slice images. Since ICH lesions often extend across multiple slices, using only single-slice images may result in reduced diagnostic accuracy by neglecting spatial continuity. Our approach addresses this limitation by integrating multi-slice information through a 9-channel pseudo-color map. To address this limitation, we explored the use of a 9-channel pseudo-color map for the discrimination of ICH in CT. [Method] A total of 21,744 cases (normal controls: 12,862; abnormal cases: 8882) from an open dataset were utilized for model training and validation. Abnormal cases included a variety of ICHs. The 9-channel pseudo-color map was generated by combining three different window settings with three continuous slices. ResNeXt50-32x4d architecture with five-fold cross-validation used. A total of 956 clinical cases were used for model testing. [Result] A total of 558,738 images were included in the model training process. The optimal model performance metrics were as follows: accuracy: 95.92%, sensitivity: 96.37%, and specificity: 95.24%. The average processing time for each case was recorded as 3.29 s. [Conclusions] The 9-channel pseudo-color map demonstrates high accuracy in the discrimination of ICH in CT images using deep learning methodologies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Radiation Science, Radiology and Radiation Oncology)
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