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Biomedicines
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Imaging Technology for Human Diseases
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Imaging Technology for Human Diseases

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 7883

Editor

Dr. Emanuele David

E-Mail Website
Guest Editor
Radiology Unit 1, Department of Medical Surgical Sciences and Advanced Technologies “GF Ingrassia”, University Hospital “Policlinico G. Rodolico”, University of Catania, 95123 Catania, Italy
Interests: ultrasound; MRI; gynecology; head and neck; vascular; neuroradiology; musculoskeletal; senology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Imaging Technology for Human Diseases” focuses on the growing importance of technological advances in diagnostics.

We know how diagnostic accuracy surely comes from precise diagnosis that only experience combined with state-of-the-art equipment can give us.

The purpose of this Special Issue is to bring together the experience of various hospital and/or university centers in the field of imaging, from X-ray to mammography, CT, MRI or ultrasound, to angiography, where the aid of new technologies can facilitate diagnosis and treatment in the field of human pathology. 

Dr. Emanuele David
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. Biomedicines 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 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

  • technological advances
  • imaging
  • X-ray
  • mammography
  • CT
  • MRI
  • ultrasound
  • angiography

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Published Papers (6 papers)

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Research

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31 pages, 33148 KB  
Article
Learning Periodic Patterns in ECG Signals Using TimesNet for Automated Cardiac Classification
by Manjur Kolhar, Raisa Nazir Ahmed Kazi and Ahmed M. Al Rajeh
Biomedicines 2026, 14(6), 1198; https://doi.org/10.3390/biomedicines14061198 - 26 May 2026
Viewed by 449
Abstract
Background/Objectives: Although deep learning methods have achieved promising performance in recent years, comparatively less attention has been given to explicitly modeling periodic and multi-scale temporal dynamics for ECG-specific representation learning within TimesNet-based frameworks. In this work, we propose an ECG-specific TimesNet-based framework [...] Read more.
Background/Objectives: Although deep learning methods have achieved promising performance in recent years, comparatively less attention has been given to explicitly modeling periodic and multi-scale temporal dynamics for ECG-specific representation learning within TimesNet-based frameworks. In this work, we propose an ECG-specific TimesNet-based framework for multi-label classification of multi-lead ECG recordings that incorporates periodicity-aware temporal modeling. Methods: The proposed framework utilizes Fast Fourier Transform (FFT)-guided temporal decomposition to identify dominant frequency components and reshapes ECG sequences into period-aligned representations to better capture intra-period morphological patterns and inter-period rhythm dependencies. Multi-scale convolutional TimesBlocks are further employed to learn rhythm-aware and morphology-aware temporal representations. Results: The proposed framework was evaluated on the PTB-XL dataset using two experimental settings: Three-Class classification (NORM, AFIB, PVC) and Five-Class classification (NORM, AFIB, MI, PVC, STTC). Experiments were conducted using a one-vs-rest multi-label learning strategy with independent class probability estimation. The framework achieved mean one-vs-rest test AUC values of 0.956 and 0.913 for the Three-Class and Five-Class settings, respectively. Experimental results indicated that the reduced classification complexity in the Three-Class setting was associated with improved feature separability, more stable decision boundaries, and enhanced discriminative representation learning. Latent-space visualization using UMAP and PCA demonstrated clearer clustering in the Three-Class configuration, while gradient-based interpretability analysis highlighted physiologically relevant ECG waveform regions contributing to model predictions. In addition, computational profiling demonstrated practical feasibility with approximately 1.957 million trainable parameters, 13.14 GFLOPs computational complexity, 5.230 ms average inference latency per ECG recording, and a throughput of approximately 191 ECG recordings per second on GPU hardware. Conclusions: These findings suggest that periodicity-aware temporal modeling can improve ECGF representation learning while demonstrating practical potential for computationally efficient and interpretable automated ECG analysis applications. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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25 pages, 2531 KB  
Article
FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting
by André Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Guilherme Dantas Bispo, Vinícius Pereira Gonçalves, Geraldo Pereira Rocha Filho, Maria Gabriela Mendonça Peixoto, Rodrigo Bonacin and Rodolfo Ipolito Meneguette
Biomedicines 2026, 14(3), 713; https://doi.org/10.3390/biomedicines14030713 - 19 Mar 2026
Cited by 1 | Viewed by 726
Abstract
Background/Objectives: Federated learning has emerged as a promising paradigm for enabling collaborative artificial intelligence in healthcare while preserving data privacy. However, most existing frameworks focus on isolated tasks and lack integrated pipelines that combine classification, segmentation, explainability, and automated clinical reporting. Methods: This [...] Read more.
Background/Objectives: Federated learning has emerged as a promising paradigm for enabling collaborative artificial intelligence in healthcare while preserving data privacy. However, most existing frameworks focus on isolated tasks and lack integrated pipelines that combine classification, segmentation, explainability, and automated clinical reporting. Methods: This study proposes FedIHRAS, a privacy-preserving federated learning framework designed for multi-institutional radiological analysis. The system integrates multi-task deep learning modules, including pathology classification using a modified ResNet-50 backbone, anatomical segmentation, explainability through Grad-CAM, and automated report generation supported by semantic aggregation using SNOMED CT. The framework employs confidence-weighted aggregation, differential privacy mechanisms, and secure aggregation protocols to ensure privacy and robustness across heterogeneous institutional datasets. Results: Experimental evaluation was conducted across four large-scale chest X-ray datasets representing simulated institutional nodes, totaling approximately 874,000 images. FedIHRAS achieved high diagnostic performance with strong cross-institutional generalization and demonstrated improved robustness under non-IID data distributions. Additional experiments showed favorable communication efficiency, effective privacy–utility trade-offs, and strong agreement with expert radiologist assessments. Conclusion: The proposed FedIHRAS framework demonstrates that federated learning can support scalable, privacy-preserving, and clinically meaningful radiological AI systems. By integrating multi-task learning, explainability, and automated reporting within a unified federated architecture, the framework addresses key limitations of existing approaches and contributes to the development of collaborative AI in healthcare. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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16 pages, 5122 KB  
Article
Non-Contrast Radiation-Free NIR Lung Imaging
by Jiří Votruba, Martin Drahanský, Tomáš Goldmann, Tomáš Brůha and Radim Kolář
Biomedicines 2025, 13(11), 2757; https://doi.org/10.3390/biomedicines13112757 - 11 Nov 2025
Viewed by 1060
Abstract
Background/Objectives: Early localization of solitary pulmonary nodules (SPNs) remains challenging despite technological advances in endoscopic navigation, as the procedure often necessitates multiple ionizing imaging examinations. This study aimed to develop and evaluate a radiation-free optical method for SPN localization based on near-infrared [...] Read more.
Background/Objectives: Early localization of solitary pulmonary nodules (SPNs) remains challenging despite technological advances in endoscopic navigation, as the procedure often necessitates multiple ionizing imaging examinations. This study aimed to develop and evaluate a radiation-free optical method for SPN localization based on near-infrared (NIR) translumination. Methods: A miniaturized NIR light source was introduced into the bronchial tree to illuminate the lung parenchyma. The transmitted and scattered NIR light was detected in real time from the pleural side using minipleuroscopy and a CMOS camera. The approach exploits intrinsic differences in optical absorption and scattering between normal and pathological lung tissue, allowing visualization of the parenchymal micro-architecture without exogenous contrast agents. Results: In ex vivo porcine lungs, tissue structures were clearly visualized through up to approximately 4 cm of parenchyma. In a ventilated pig (n = 1), bronchial NIR illumination was consistently detected from the pleural cavity and produced distinct images of lobular structures and the bronchial mucosa. Conclusions: These feasibility findings demonstrate that NIR translumination can provide radiation-free intra-thoracic visualization and may serve as a valuable adjunct for biopsy guidance. Further quantitative validation and clinical translation are warranted to establish its applicability in human pulmonary procedures. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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14 pages, 1477 KB  
Article
Mammographic Calcifications in Lung Transplant Recipients: Prevalence and Evolution
by Jonathan Saenger, Jasmin Happe, Caroline Maier, Bjarne Kerber, Ela Uenal, Denise Bos, Thomas Frauenfelder and Andreas Boss
Biomedicines 2025, 13(9), 2318; https://doi.org/10.3390/biomedicines13092318 - 22 Sep 2025
Cited by 1 | Viewed by 1025
Abstract
Objective: To investigate the prevalence and progression of macrocalcifications or sporadic scattered microcalcifications, breast arterial calcifications (BAC) and grouped microcalcifications in women undergoing lung transplantation (LTX). Materials and Methods: In this retrospective single-center cohort study, 176 adult female patients who underwent mammography between [...] Read more.
Objective: To investigate the prevalence and progression of macrocalcifications or sporadic scattered microcalcifications, breast arterial calcifications (BAC) and grouped microcalcifications in women undergoing lung transplantation (LTX). Materials and Methods: In this retrospective single-center cohort study, 176 adult female patients who underwent mammography between 2008 and 2025 were included: 82 LTX recipients and 94 age-matched controls. Mammographic findings were assessed using standardized BI-RADS criteria and a visual BAC scoring system. Clinical and demographic data were extracted from electronic medical records. Multivariable logistic regression and cumulative incidence analysis were used to evaluate associations and progression patterns. Interobserver agreement was assessed using Fleiss’ kappa. Results: BAC and grouped microcalcifications were significantly more prevalent in the LTX group in the last mammography (BAC: OR 6.57, 95% CI 2.34–20.7; microcalcifications: OR 14.6, 95% CI 3.93–73.9; both p < 0.001). Cumulative incidence analysis showed accelerated progression of BAC and grouped microcalcifications in LTX recipients (p ≤ 0.01), while macrocalcifications or sporadic scattered microcalcification progression did not differ significantly. BAC was often more extensive and potentially mimicked malignant findings. Interobserver agreement was highest for the four-level BAC scoring system (κ = 0.61), followed by BAC presence (κ = 0.59) and macrocalcifications (κ = 0.51), while grouped microcalcifications showed only fair agreement (κ = 0.33). Conclusions: Lung transplant recipients demonstrate significantly higher prevalence and faster progression of BAC and grouped microcalcifications compared to controls, complicating mammographic interpretation. Given their elevated risk of aggressive malignancies and diagnostic overlap between benign and suspicious calcifications, transplant recipients may benefit from tailored screening strategies. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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15 pages, 2355 KB  
Article
Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes?
by Silvia Gigli, Emanuele David, Giacomo Bonito, Luisa Favale, Silvia di Sero, Antonio Vinci, Lucia Manganaro and Paolo Ricci
Biomedicines 2025, 13(6), 1364; https://doi.org/10.3390/biomedicines13061364 - 2 Jun 2025
Cited by 1 | Viewed by 1386
Abstract
Introduction: A potential prognostic biomarker for predicting the response to immunotherapy in breast cancer (BC) is tumor-infiltrating lymphocytes (TILs). The purpose of this research is to examine if preoperative characteristics of breast magnetic resonance imaging (MRI) may be used to predict TIL levels [...] Read more.
Introduction: A potential prognostic biomarker for predicting the response to immunotherapy in breast cancer (BC) is tumor-infiltrating lymphocytes (TILs). The purpose of this research is to examine if preoperative characteristics of breast magnetic resonance imaging (MRI) may be used to predict TIL levels in a group of BC patients. In addition, we aimed to assess any potential relationship between the various tumor biology subgroups and MR imaging characteristics. Materials and Methods: This retrospective analysis comprised 145 participants with histologically confirmed BC who had preoperative DCE MRI. We collected and examined patient information as well as tumor MRI features, such as size and shape, edema, necrosis, multifocality/multicentricity, background parenchymal enhancement (BPE), and apparent diffusion coefficient (ADC) values. We divided patients into two groups based on their TIL levels: low-TIL (<10%) and high-TIL groups (≥10%). Following core needle biopsy, tumors were categorized as Luminal A, Luminal B, HER2+, and Triple Negative using immunohistochemical analysis. TIL levels were correlated with tumor biological profiles and MRI features using both parametric and non-parametric tests. Results: Patients were categorized as having a high TIL level (≥10%; 54/145 patients) and a low TIL level (<10%; 91/145 patients) based on the median TIL level of 10%. Of the lesions, 13 were HER2-positive, 16 were Triple Negative, 49 were Luminal A, and 67 were Luminal B. Higher TIL levels were statistically correlated with TNBC (11/16 individuals, p: 0.007). ADC values (p = 0.01), BPE levels (p = 0.008), and TIL levels were all significantly negatively correlated. Significantly more homogenous enhancement was seen in tumors with elevated TIL levels (p = 0.001). The ADC values and the enhancing characteristics were the most important factors in predicting TIL levels, according to logistic regression analysis, and when combined, they demonstrated the strongest ability to distinguish between the two groups (AUC = 0.744). Conclusions: MRI features, particularly ADC values and enhancement characteristics, may play a pivotal role in the assessment of TIL levels in BC before surgery. This could help patients to better customize treatments to the features of their tumors. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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Review

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23 pages, 2082 KB  
Review
Point-of-Care Transesophageal Echocardiography in Emergency and Intensive Care: An Evolving Imaging Modality
by Debora Emanuela Torre and Carmelo Pirri
Biomedicines 2025, 13(11), 2680; https://doi.org/10.3390/biomedicines13112680 - 31 Oct 2025
Cited by 6 | Viewed by 2567
Abstract
Transesophageal echocardiography (TEE) has long been established as a cornerstone imaging modality in cardiac surgery and perioperative medicine. In recent years, however, its role has expanded into emergency and intensive care settings, where rapid and accurate hemodynamic assessment is crucial for survival. Point-of-care [...] Read more.
Transesophageal echocardiography (TEE) has long been established as a cornerstone imaging modality in cardiac surgery and perioperative medicine. In recent years, however, its role has expanded into emergency and intensive care settings, where rapid and accurate hemodynamic assessment is crucial for survival. Point-of-care TEE provides advantages over transthoracic echocardiography when acoustic windows are limited, particularly in mechanically ventilated or critically unstable patients, allowing continuous high-quality visualization of cardiac function, volume status, and great vessel pathology to guide immediate therapeutic interventions. This narrative review examines the evolving role of TEE in acute settings, with emphasis on its application in shock, cardiac arrest, pulmonary embolism, tamponade, and its value in extracorporeal membrane oxygenation (ECMO) cannulation. Advances such as three-dimensional TEE, miniaturized probes, and the integration of artificial intelligence are also discussed, as potential drivers of innovation. While bridging technological progress with clinical practice, TEE emerges as a versatile tool in critical care. However, its broader adoption is still limited by probe availability, operator training, and institutional resources. Overcoming these barriers will be essential to translating technological advances into widespread practice. Full article
(This article belongs to the Special Issue Imaging Technology for Human Diseases)
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