Search Results (602)

Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures—even at very low levels, termed “subconcussive”—can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain’s largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose–response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management. Full article

Due to the health information asymmetry, the upsurge of Patient Online Communities (POCs) and Patient Social Media groups has increased the importance of electronic word-of-mouth (eWOM) in health care, influencing individuals’ health decisions, as well as a medical organization’s image. This study investigates the association between the multidimensional perceived value of patients and their eWOM intentions in health care services, based on Art Weinstein’s adapted Perceived Value framework. According to this framework, perceived value comprises perceived quality, perceived service outcome, non-monetary costs, and organizational image. Data were collected from 210 Cardiology patients and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings of this study revealed that perceived value is positively associated with eWOM intentions within this sample, which highlights the practical importance of enhancing patient experience. As perceived value improves, it may be associated with increased patient-generated content in the form of eWOM. This study provides practical insights and contributes to the understanding of the patients’ perceived value in engaging in health-related eWOM. Full article

Background/Objectives: An accurate understanding of anterior cruciate ligament (ACL) morphology is essential for individualized surgical planning in ACL reconstruction. Morphometric parameters of the knee, including the femoral notch width and surrounding soft tissue characteristics, may influence native ACL dimensions and potentially assist in preoperative graft sizing. Methods: This retrospective case–control study analyzed medical records, radiographs, and knee magnetic resonance imaging (MRI) performed at a tertiary academic medical center. Variables collected included femoral notch width, thigh thickness, and ACL attachment dimensions at the femoral and tibial insertions. Comparisons between patients with ACL tears and those with intact ACLs were also performed. Correlation analyses were performed to evaluate associations between morphometric parameters and ACL attachment size. Multivariable linear regression models were constructed to identify independent predictors after adjusting for age, sex, body mass index (BMI), limb side (left or right leg), and ACL status. Results: A total of 600 participants were included. The mean femoral notch width was 21.55 ± 6.15 mm, and the mean thigh thickness was 53.05 ± 11.66 mm. The mean ACL femoral and tibial attachment sizes were 8.12 ± 2.57 mm and 11.79 ± 3.89 mm, respectively. Thigh thickness demonstrated weak but significant positive correlations with both ACL femoral (r = 0.168, p = 0.001) and tibial attachment sizes (r = 0.236, p < 0.001). Femoral notch width showed a borderline association with ACL femoral attachment size (r = 0.092, p = 0.068) and a weak but significant correlation with ACL tibial attachment size (r = 0.095, p = 0.039). ACL tear group exhibited smaller thigh thickness measurements compared with controls (49.80 ± 12.00 mm vs. 55.65 ± 14.80 mm, p < 0.001) and smaller femoral notch width measurements compared with controls (21.20 ± 3.40 mm vs. 22.50 ± 3.18 mm, p = 0.001). Moreover, further analysis demonstrated that ACL tear status was associated with smaller measured ACL attachment sizes (p < 0.001). Conclusions: Thigh thickness and femoral notch width demonstrate measurable association with ACL attachment dimensions and differ between patients with ACL tears and those with intact ligaments. These findings indicate that both osseous and soft-tissue morphometric characteristics may influence ACL morphology and susceptibility to injury. Comprehensive preoperative imaging assessment of these anatomical parameters may help to optimize individualized surgical planning in ACL reconstruction. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Heart failure (HF) is a multifactorial and heterogeneous syndrome with substantial epidemiological burden, high mortality, and impact on quality of life. In the context of heart failure, left ventricular ejection fraction (LVEF) has been regarded as the most important marker of systolic function and is fundamental in medical research and clinical practice. In research, LVEF has been a major inclusion criterion in most clinical trials over the past few decades. Furthermore, international heart failure guidelines rely on LVEF for the diagnosis of HF and to guide effective treatment. Additionally, our understanding of HF phenotypes and prognosis is mostly grounded in a classification based on LVEF. Nevertheless, there has been a growing debate regarding the role of LVEF in heart failure. In this context, the purpose of this review is to discuss both the advantages and contemporary relevance of LVEF in heart failure, as well as its limitations and controversies. In addition, this review aims to discuss potential alternatives and future directions in heart failure classification, such as new classification methods, alternative measurements of systolic function and imaging techniques, the HLM score, and the use of artificial intelligence and machine learning. Full article

Hemodynamic analysis of blood flow is critical for diagnosing cardiovascular diseases and investigating cardiovascular parameters, such as aneurysms and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI), respectively. Computational fluid dynamics (CFD), on the other hand, can be used to generate blood flow simulations. To generate and analyze subject-specific blood flow simulations, MRI and CFD have to be brought together. We present an interactive, customizable, and user-oriented visual analysis tool that integrates measured data and CFD simulations. Thus, our open-source tool supports both medical and numerical analysis workflows. It enables the creation of simulation ensembles with a high variety of parameters. Furthermore, it allows for visual and analytical examination of simulations and measurements through 2D embeddings. To demonstrate the effectiveness of our tool, we applied it to three real-world use cases, showcasing its ability to configure simulation ensembles and analyze blood flow. We evaluated our example cases together with MRI and CFD experts. By combining the strengths of both CFD and MRI, our tool provides a comprehensive understanding of hemodynamic parameters, facilitating accurate analysis of hemodynamic biomarkers. Full article

Structured radiology reporting can improve clinical decision support by standardizing clinical findings into hierarchical formats. However, thousands of questions in structured report templates about clinical findings are prohibitively time-consuming, which can limit clinical adoption. Furthermore, early medical VQA datasets primarily focused on free-text and independent question–answer pairs while a recent dataset, Rad-ReStruct, introduced a hierarchical VQA, but the accompanying model still relies heavily on flattened embedding representations and single-path text–image fusion mechanisms that inadequately handle complex hierarchical dependencies in responses. In this paper, we propose DPA-HiVQA (Dual-Path Cross-Attention for Hierarchical VQA), addressing these limitations through two key contributions: (1) multi-scale image embedding representing global semantic embeddings with patch-level spatial features from domain-specific BioViL encoder; (2) dual-path cross-attention mechanism enabling simultaneous holistic semantic understanding and fine-grained spatial reasoning. Evaluated on the Rad-ReStruct benchmark, the model substantially outperforms the established benchmark baseline with an overall F1-score and Level 3 F1-score improvement by 21.2% and 31.9%, respectively. The proposed model demonstrates that dual-path cross-attention architectures can effectively connect holistic semantic understanding and fine-grained spatial detail, paving the way for practical AI-assisted structured reporting systems that reduce radiologist burden while maintaining diagnostic accuracy. Full article

Background: Oral and cranio-maxillofacial (OCMF) surgery is a complex specialty that requires detailed anatomical knowledge and, in fracture care, the ability to interpret imaging accurately. Mixed reality (MR) may improve spatial understanding in anatomy-based disciplines, but its value for teaching mandibular fracture classification remains uncertain. Methods: Medical and dental students at the University of Zurich were randomized 1:1 to classify four unilateral mandibular fractures using either MR or conventional two-dimensional (2D) imaging. Primary outcomes were perceived usefulness, ease of use, learning, and user satisfaction, assessed with a 15-item usability questionnaire. Secondary outcomes were fracture-classification accuracy and time to fracture classification. Results: Forty medical and dental students were included. Baseline characteristics were comparable between groups, and overall fracture-classification accuracy did not differ significantly between MR and 2D. Both groups became faster across successive cases, indicating a learning effect, although the 2D group completed classifications more quickly overall. MR participants reported higher scores for learning and user satisfaction, whereas the 2D group rated ease of use more favorably. Conclusions: MR increased user satisfaction but did not improve fracture-classification accuracy compared with 2D imaging. When integrated thoughtfully into OCMF education, MR may complement, rather than replace, conventional imaging approaches. Full article

Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation for understanding why fusion improves prediction, how information is distributed across modalities, and when models can be trusted under incomplete or shifting data. This paper develops a unified information-theoretic framework that formalizes multimodal biomedical learning as an information optimization problem. We formulate multimodal representation learning through the information bottleneck principle, deriving a variational objective that balances predictive sufficiency against informational compression in an architecture-agnostic manner. Building on this foundation, we introduce information-theoretic tools for decomposing modality contributions via conditional mutual information, quantifying redundancy and synergy, and diagnosing fusion collapse. We further show that robustness to missing modalities can be cast as an information consistency problem and extend the framework to longitudinal disease modeling through transfer entropy and sequential information bottleneck objectives. Applications to multimodal foundation models, uncertainty quantification, calibration, and out-of-distribution detection are developed. Empirical case studies across three biomedical datasets (TCGA breast cancer multi-omics, TCGA glioma clinical-plus-molecular data, and OASIS-2 longitudinal Alzheimer’s data) show that the framework’s key quantities are computable and interpretable on real data: MI decomposition identifies modality dominance and redundancy; the VMIB traces a compression–prediction tradeoff in the information plane; entropy-based selective prediction raises accuracy from 0.787 to 0.939 at 50% coverage; transfer entropy reveals stage-dependent modality influence in disease progression; and pretraining/adaptation diagnostics distinguish efficient from wasteful fine-tuning strategies. Together, these results develop entropy and mutual information as organizing principles for the design, analysis, and evaluation of multimodal biomedical AI systems. Full article

The dynamic field of radiopharmaceuticals is currently experiencing an explosion of growth due in part to excitement over the emerging field of theranostics (therapy and diagnostics). Radiopharmaceuticals use physiological targeting methods to deliver radionuclides with medically relevant decay properties to disease biomarkers for diagnosis and treatment, offering opportunities for early disease imaging and radiation therapy treatment in disease pathologies that are inoperable or refractory to other forms of radiotherapy. Sustaining this rapidly growing field depends heavily on the continued design and production of novel, effective radiopharmaceuticals. Effective therapeutic radiopharmaceuticals cause complex and varied cellular responses, and to choose radionuclides that maximize therapeutic response, researchers must understand radiation biology. Cellular radiation response depends heavily on factors including linear energy transfer (LET), dose, dose rate, targeted location, direct or indirect energy deposition mechanisms, the broader cellular matrix, cellular stress signaling pathways, and endogenous radiation protection mechanisms. Because of the extensive application of low-LET external beam radiation on clinical cancer treatments, biological responses to low-LET form the basis of radiation biology and are generally considered transferable to high-LET radiopharmaceuticals. However, increased focus on high-LET, radiopharmaceutical therapy-specific radiation biology is motivated by differences between low- and high-LET radiation, external beam versus radiopharmaceutical therapy-induced biological response, and the observed varied clinical responses to radiopharmaceutical therapies. This review article summarizes historical understanding of low- and high-LET radiation responses within cells, with emphasis on radiopharmaceutical-specific responses when available, and discusses current gaps in understanding in the radiation biology of radiotheranostic pharmaceuticals. Full article

Background: The complex anatomy of the head and neck region challenges medical students. Cinematic rendering (CR) is an advanced visualization technique that enables three-dimensional (3D) representation of cross-sectional image data and is used in education at the Faculty of Medicine at Johannes Kepler University. Methods: For the first time, CR images were used to illustrate otolaryngology anatomy in medical education. The educational value of this approach was evaluated using a questionnaire assessing six core statements and dichotomous variables, including prior experience with CR and otolaryngology. Results: All six statements showed high levels of agreement based on mean evaluation scores. Evaluation results differed according to participants’ prior experience with CR. A strong correlation was exploratorily observed between prior experience with CR and improved spatial awareness of otolaryngology anatomy (ρ = 0.80, p < 0.05). Additionally, prior experience with CR correlated with improved understanding of complex disease processes (ρ = 0.76, p < 0.05) and enhanced general comprehension of the respective field (ρ = 0.74, p < 0.05). Conclusions: These findings suggest that early integration of CR into otolaryngology education may support students’ perceived spatial understanding and facilitate comprehension of complex disease processes. Full article

Background/Objectives: Simulation digital twins (DT) models that integrate patient-specific imaging with artificial intelligence (AI)-based segmentation and extended reality (XR) technologies are rapidly increasing in relevance in personalized medicine. While their clinical applications are expanding, their role as reusable educational tools and the technical pipeline utilized for their development remain incompletely characterized. This narrative review examines current approaches to digital twin creation and XR integration, illustrated by a scoliosis-specific proof-of-concept educational case study. Methods: A narrative technical review was conducted by identifying relevant search keywords within the fields of AI-based image segmentation, extended reality in medicine, and medical education based on the authors’ expertise and familiarity with the subject. PubMed, Google Scholar, and Scopus were searched for English-language studies published primarily between 2015 and 2025 addressing patient-specific three-dimensional modeling, AI-driven segmentation, and XR applications in spine, orthopedic, anesthesiology, and interventional care. A de-identified case of scoliosis is used to present a proof-of-concept example of this process of creating a simulated digital twin for the purpose of medical education in a recorded XR format. Results: Prior studies demonstrated benefits of patient-specific 3D models for anatomical understanding and procedural planning, while highlighting limitations in segmentation accuracy and workflow integration. Nevertheless, while DTs have traditionally served clinical roles in surgical planning or pre-procedural rehearsal, their pedagogical potential remains under-explored. In the proof-of-concept case study, AI-assisted segmentation enabled rapid creation of an anatomically detailed scoliosis digital twin that was incorporated into XR and used to produce a reusable, spatially anchored instructional experience focused on neuraxial access. Conclusions: AI-enabled digital twin models integrated with XR represent a promising approach for personalized, anatomy-driven medical education. Further evaluation is needed to assess educational outcomes, scalability, and integration into clinical training workflows. Full article

Background: Contemporary dentistry increasingly relies on tools and methods derived from the exact sciences, particularly mathematics and physics, to better understand the complexity of biological processes. One such tool is fractal analysis (FA), which enables the characterization and quantification of irregular, complex, self-similar structures commonly observed in nature in the form of the fractal dimension (FD). In oral radiology, it has been found useful for describing structural changes in bone tissue. Objective: The aim of this review is to present the current state of knowledge regarding the application of fractal analysis in the management of patients with, or at risk for, medication-related osteonecrosis of the jaw (MRONJ), with particular emphasis on its diagnostic and prognostic potential. This paper summarizes key research findings, and discusses the principal challenges and limitations associated with the use of this method of analysis in MRONJ cases. Materials and Methods: The inclusion criteria were as follows: original papers, the presence of MRONJ, and fractal analysis. In order to find relevant studies, international databases, including PubMed and Google Scholar, were searched. The last search was performed on 29 November 2025. Six articles were included in the systematic review. Results: The majority of the review studies show lower FD values for MRONJ patients and healthy control groups. The values are the lowest for necrotic lesions and highest for perinecrotic bone tissue. Conclusions: FD values calculated from radiological images of the jaws can be used to differentiate healthy and MRONJ-affected patients and to describe necrotic lesions. Fractal analysis has potential to be used in the diagnosis and monitoring of MRONJ after further studies and standardization of methodology. Full article

This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare. Full article

Understanding the hemodynamics of the carotid artery is essential for assessing atherosclerotic disease progression and identifying regions vulnerable to plaque formation. Background: Disturbed flow patterns and abnormal shear stresses, particularly near the carotid bifurcation, are known to influence endothelial dysfunction; therefore, this study aims to quantify the impact of patient-specific carotid artery geometry on key hemodynamic parameters associated with atherosclerotic risk. Methods: Four patient-specific carotid artery geometries were reconstructed from medical imaging data, processed using MIMICS, and analyzed using computational fluid dynamics in ANSYS Fluent, with blood modeled as an incompressible non-Newtonian fluid using the Carreau–Yasuda viscosity model under pulsatile flow conditions; velocity streamlines, pressure distribution, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated at early systole, peak systole, and peak diastole. Results: The simulations revealed complex flow behaviour, including flow reversal, pressure build-up, and low-shear regions concentrated near the carotid bulb and bifurcation, with TAWSS consistently identifying low-shear zones (<1 Pa) across all geometries and OSI exhibiting pronounced directional oscillations in models with increased curvature and wider bifurcation angles. Conclusions: These findings demonstrate that geometric characteristics such as bifurcation angle, vessel tortuosity, and asymmetry play a critical role in shaping local haemodynamics, underscoring the utility of patient-specific CFD analysis as a diagnostic and predictive tool for atherosclerotic risk assessment and supporting more informed, personalized clinical decision-making. Full article