Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
3.8
Journals
Diagnostics
Special Issues
Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical...
share announcement

Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical Integration

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3606

Editor

Dr. Kyong Joon Lee

E-Mail Website
Guest Editor
Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
Interests: medical image analysis; artificial intelligence; data science

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), particularly deep learning, is rapidly transforming the landscape of medical diagnostics. In recent years, AI algorithms have demonstrated remarkable capabilities in analyzing complex medical images—such as CT, MRI, and radiographs—with a level of accuracy and efficiency that can augment, and in some cases exceed, human performance. This technological revolution promises to enhance diagnostic precision, streamline clinical workflows, and pave the way for more personalized patient care. From early cancer detection in radiology to subtle anomaly identification in pathology, AI is becoming an indispensable tool for modern medicine.

This Special Issue aims to showcase the latest research, innovations, and clinical applications of AI in medical imaging. We invite submissions of original research articles and comprehensive reviews that explore novel algorithms, clinical validation studies, and new frontiers in computer-aided detection and diagnosis. We are particularly interested in works that address current challenges, such as model interpretability, data heterogeneity, and seamless integration into clinical practice. The goal of this Special Issue is to bring together cutting-edge developments that are pushing the boundaries of advanced diagnostics and shaping the future of healthcare.

We look forward to receiving your contributions.

Dr. Kyong Joon 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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
  • medical imaging
  • deep learning
  • computer-aided diagnosis (CADx)
  • diagnostic imaging
  • clinical validation
  • medical image analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 3486 KB  
Article
Prediction of Anemia in Adenomyosis Patients Using Transvaginal Ultrasound Radiomics
by Chaeheon Lee, Young Jae Kim, Han-song Song, Soohyun Oh and Kwang Gi Kim
Diagnostics 2026, 16(17), 2827; https://doi.org/10.3390/diagnostics16172827 - 2 Sep 2026
Viewed by 201
Abstract
Background: Abnormal uterine bleeding (AUB) caused by adenomyosis can result in significant iron-deficiency anemia. Given that transvaginal ultrasound (TVUS) is commonly performed during routine examinations, its assessment may facilitate preemptive treatment. Methods: In this study, TVUS images from patients with surgically confirmed adenomyosis [...] Read more.
Background: Abnormal uterine bleeding (AUB) caused by adenomyosis can result in significant iron-deficiency anemia. Given that transvaginal ultrasound (TVUS) is commonly performed during routine examinations, its assessment may facilitate preemptive treatment. Methods: In this study, TVUS images from patients with surgically confirmed adenomyosis were preprocessed to minimize intra- and inter-scan variability. Radiomics features were extracted using PyRadiomics to train automated machine-learning classifiers under k-fold nested cross-validation. Multiple dataset configurations were evaluated, including feature extraction from a custom region-of-interest (ROI) of the uterine corpus, whole-image features adjusted for uterine size, and supplementary variables from complete blood counts (CBC). Results: Radiomics-centered models demonstrated modest discrimination (mean accuracy 0.606–0.618). While incorporating CBC variables with radiomics features substantially improved performance compared to radiomics alone, models trained exclusively on CBC data yielded overall higher results. Conclusions: These findings indicate that quantitative features derived from routine TVUS provide modest complementary information for predicting anemia. While they do not offer clear incremental value when highly discriminatory clinical data (CBC) are available, TVUS radiomics may serve as a supplementary diagnostic tool in settings where blood tests are incomplete or unavailable. Further validation in larger, multi-institutional cohorts with patient-level separation is warranted. Full article
(This article belongs to the Special Issue Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical Integration)
►▼ Show Figures

Figure 1

12 pages, 2066 KB  
Article
Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep Learning Model: A Retrospective CBCT Study
by Furkan Talo, Nurullah Duger, Emre Aslan, Muhammed Yildirim, Mahmut Kaya, Ahmet Bedri Ozer and Tuba Talo Yildirim
Diagnostics 2026, 16(10), 1512; https://doi.org/10.3390/diagnostics16101512 - 16 May 2026
Viewed by 507
Abstract
Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic [...] Read more.
Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors. Full article
(This article belongs to the Special Issue Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical Integration)
►▼ Show Figures

Figure 1

12 pages, 3762 KB  
Article
Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging
by Siwoo Nam and Sang Hyun Park
Diagnostics 2026, 16(9), 1370; https://doi.org/10.3390/diagnostics16091370 - 30 Apr 2026
Viewed by 559
Abstract
Background/Objectives: Precise nuclei instance segmentation is a prerequisite for reliable digital pathology, yet the scarcity of pixel-level annotations remains a significant bottleneck for deep learning models. Methods: We propose a self-evolving framework for robust nuclei segmentation that uses only sparse point [...] Read more.
Background/Objectives: Precise nuclei instance segmentation is a prerequisite for reliable digital pathology, yet the scarcity of pixel-level annotations remains a significant bottleneck for deep learning models. Methods: We propose a self-evolving framework for robust nuclei segmentation that uses only sparse point annotations, extending the Segment Anything Model (SAM). To overcome the limitations of static pseudo-labels, our method introduces a self-evolving labeling strategy via Exponential Moving Average (EMA), which adaptively refines learning targets. We also integrate instance-aware contrastive learning using point prompts as spatial anchors and implement a consensus-based filtering mechanism between prompt-guided and prompt-free decoders. Results: Extensive evaluations on CPM17, MoNuSeg, and the challenging CoNSeP datasets demonstrate that our framework achieves state-of-the-art performance across various backbones, including ViT-B and ViT-H. Conclusions: By enabling a seamless transition from general-purpose foundation models to specialized histopathology experts, this self-refining approach delivers a highly efficient, accurate solution for automated diagnostic workflows in clinical settings. Full article
(This article belongs to the Special Issue Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical Integration)
►▼ Show Figures

Figure 1

17 pages, 2032 KB  
Article
AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network
by Jeong Sub Lee, Euijin Hwang, Changgyun Jin, Kyong Joon Lee, Ye Ra Choi and Sang Il Choi
Diagnostics 2026, 16(4), 524; https://doi.org/10.3390/diagnostics16040524 - 9 Feb 2026
Viewed by 1292
Abstract
Background: High false-positive rates remain a significant challenge in the automated detection of pulmonary embolism (PE) using Computed Tomography Pulmonary Angiography (CTPA). This study evaluated the additional value of a False-Positive Reduction (FPR) module integrated into a Region Proposal Network (RPN). Methods [...] Read more.
Background: High false-positive rates remain a significant challenge in the automated detection of pulmonary embolism (PE) using Computed Tomography Pulmonary Angiography (CTPA). This study evaluated the additional value of a False-Positive Reduction (FPR) module integrated into a Region Proposal Network (RPN). Methods: A retrospective analysis of 303 CTPA scans (163 PE-positive and 140 PE-negative) was conducted from a single tertiary institution. Both models were additionally validated on an independent external cohort of 100 CTPA scans (50 PE-positive and 50 PE-negative) from the RSNA PE Challenge dataset. The diagnostic performance of the one-stage RPN-only model was compared with that of a two-stage Modified Mask R-CNN (Region-based Convolutional Neural Network) incorporating the FPR module. Results: The Modified Mask R-CNN exhibited significant improvement in terms of specificity. The false-positive rate per scan decreased by 31% in comparison to the RPN-only model. Although there was a slight reduction in patient-level sensitivity, the Positive Predictive Value significantly increased by 10.5%. Additionally, patient-level specificity for emboli with a volume ≥ 1000 mm3 increased, reflecting a 7.4% relative improvement in detecting clinically significant emboli. Conclusions: The Modified Mask R-CNN significantly reduced false positives while maintaining high sensitivity over a region proposal network. Full article
(This article belongs to the Special Issue Advancing Medical Imaging with AI: From Cutting-Edge Algorithms to Clinical Integration)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop