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J. Imaging
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Clinical and Pathological Imaging in the Era of Artificial Intelligence:...
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Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 773

Special Issue Editors

Dr. Gerardo Cazzato

E-Mail Website
Guest Editor
Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari “Aldo Moro”, 70124 Bari, Italy
Interests: skin diseases; dermatology; dermatopathology; diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Francesca Arezzo

E-Mail Website
Guest Editor
Oncological Gynecology Department, IRCCS Giovanni Paolo II Cancer Institute, 70124 Bari, Italy
Interests: gynecologic oncology; gynecological malignancy; gynecological ultrasound; artificial intelligence in gynecology; radiomics in gynecological imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The editors are grateful to the many researchers who contributed to the success of the first volume of this Special Issue (https://www.mdpi.com/journal/jimaging/special_issues/4JOJ31M8UV). We are now very pleased to announce the second edition: “Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition”.

Clinical imaging has always been one of the primary modalities of patient study, depending on the most diverse pathologies that may come to the attention of the clinical physician. On the other hand, pathology has also benefited from this investment in innovation, with the development of new instrumentation, such as digital scanners and algorithms, that can co-advise the pathologist in routine diagnostics. In this Special Issue, we aim to focus our attention on the new artificial intelligence (AI) methods that have developed precisely from imaging and that are beginning to be validated as a medical aid, not only at the patient’s bedside but also at a distance (telemedicine).

Dr. Gerardo Cazzato
Dr. Francesca Arezzo
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. Journal of Imaging 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 1800 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
  • clinical imaging
  • pathology
  • ginecopathology
  • dermatopathology

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Published Papers (2 papers)

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Research

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27 pages, 3997 KiB  
Article
NCT-CXR: Enhancing Pulmonary Abnormality Segmentation on Chest X-Rays Using Improved Coordinate Geometric Transformations
by Abu Salam, Pulung Nurtantio Andono, Purwanto, Moch Arief Soeleman, Mohamad Sidiq, Farrikh Alzami, Ika Novita Dewi, Suryanti, Eko Adhi Pangarsa, Daniel Rizky, Budi Setiawan, Damai Santosa, Antonius Gunawan Santoso, Farid Che Ghazali and Eko Supriyanto
J. Imaging 2025, 11(6), 186; https://doi.org/10.3390/jimaging11060186 - 5 Jun 2025
Viewed by 225
Abstract
Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, [...] Read more.
Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, a framework that combines anatomically constrained data augmentation with expert-guided annotation refinement. NCT-CXR applies carefully calibrated discrete-angle rotations (±5°, ±10°) and intensity-based augmentations to enrich training data while preserving spatial and anatomical integrity. To address label noise in the NIH Chest X-ray dataset, we further introduce a clinically validated annotation refinement pipeline using the OncoDocAI platform, resulting in multi-label pixel-level segmentation masks for nine thoracic conditions. YOLOv8 was selected as the segmentation backbone due to its architectural efficiency, speed, and high spatial accuracy. Experimental results show that NCT-CXR significantly improves segmentation precision, especially for pneumothorax (0.829 and 0.804 for ±5° and ±10°, respectively). Non-parametric statistical testing (Kruskal–Wallis, H = 14.874, p = 0.0019) and post hoc Nemenyi analysis (p = 0.0138 and p = 0.0056) confirm the superiority of discrete-angle augmentation over mixed strategies. These findings underscore the importance of clinically constrained augmentation and high-quality annotation in building robust segmentation models. NCT-CXR offers a practical, high-performance solution for integrating deep learning into radiological workflows. Full article
(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition)
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Review

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18 pages, 4899 KiB  
Review
Cardiac Magnetic Resonance in the Assessment of Atrial Cardiomyopathy and Pulmonary Vein Isolation Planning for Atrial Fibrillation
by Nicola Pegoraro, Serena Chiarello, Riccardo Bisi, Giuseppe Muscogiuri, Matteo Bertini, Aldo Carnevale, Melchiore Giganti and Alberto Cossu
J. Imaging 2025, 11(5), 143; https://doi.org/10.3390/jimaging11050143 - 2 May 2025
Viewed by 399
Abstract
Atrial fibrillation (AF) is the most frequently observed type of arrhythmia among adults, and its absolute prevalence is steadily rising in close association with the aging of the population, with its prevalence varying from 2% in the general population to 10–12% among the [...] Read more.
Atrial fibrillation (AF) is the most frequently observed type of arrhythmia among adults, and its absolute prevalence is steadily rising in close association with the aging of the population, with its prevalence varying from 2% in the general population to 10–12% among the elderly. The relatively new concepts of ‘atrial cardiomyopathy’ and “AF-related atrial cardiomyopathy”, along with the growing body of knowledge regarding remodeling, function, and tissue characterization, highlight the need for novel approaches to the diagnostic process as well as in the therapeutic guidance and monitoring of atrial arrhythmias. Advanced imaging techniques, particularly cardiac magnetic resonance (CMR) imaging, have emerged as pivotal in the detailed assessment of atrial structure and function. CMR facilitates the precise measurement of left atrial volume and morphology, which are critical predictors of AF recurrence post-intervention. Furthermore, it enables the evaluation of atrial fibrosis using late gadolinium enhancement (LGE), offering a non-invasive method to assess the severity and distribution of fibrotic tissue. The possibility of an accurate CMR pulmonary vein anatomy mapping enhances the precision of pulmonary vein isolation procedures, potentially improving outcomes in AF management. This review underlines the integration of novel diagnostic tools in enhancing the understanding and management of AF, advocating for a shift towards more personalized and effective therapeutic programs. Full article
(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition)
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