Search Results (234)

Objective: Glaucoma is a complex optic neuropathy characterized by the progressive loss of retinal ganglion cells (RGCs). Animal models are crucial tools for deciphering its multidimensional pathogenesis and evaluating novel therapeutic strategies. This review aims to systematically summarize the establishment methods, application advances, and future development trends of various glanimal models. Methods: The literature for this review was identified through systematic searches of electronic databases, including PubMed, Web of Science Core Collection, and Google Scholar. The search strategy utilized a combination of keywords and their variants: “glaucoma”, “animal models”, “retinal ganglion cells”, “intraocular pressure”, “neuroprotection”, “immune inflammation”, “fibrosis”, and “filtration surgery”. The search focused on articles published between 2015 and 2025 to cover the major advances of the last decade. The scope encompassed original research articles, reviews, and meta-analyses. Results: Diverse glanimal models successfully replicate different facets of glaucoma, elucidating multidimensional pathogenesis involving mechanical stress, immune inflammation, excitotoxicity, oxidative stress, and fibrosis. These models have played an indispensable role in screening neuroprotective agents, evaluating anti-fibrotic strategies, and validating the application of advanced imaging and functional assessment technologies. Current research is evolving towards model standardization, multi-factor simulation, and the integration of novel drug delivery systems and immunomodulatory strategies. Conclusions: The diversification of glanimal models provides a powerful platform for in-depth investigation of disease mechanisms and the development of innovative therapies. Future research should focus on establishing standardized models that better mimic the clinical pathological state and deeply integrating multimodal assessment technologies with targeted therapies. This will facilitate the translation of basic research into clinical applications, ultimately achieving personalized precision medicine for glaucoma. Full article

Compound meta-optics, characterized by the unprecedented complex optical architectures containing single or multiple meta-optics elements, has emerged as a powerful paradigm for overcoming the physical limitations of single-layer metasurfaces. This review systematically examines the recent progress in this burgeoning field, primarily focusing on the development of high-performance optical systems for imaging, display, sensing, and computing. We first focus on the design of compound metalens architectures that integrate metalenses with additional elements such as iris, refractive optics, or other meta-optics elements. These configurations effectively succeed in providing multiple high-quality image quality metrics simultaneously by correcting monochromatic and chromatic aberrations, expanding the field of view, enhancing overall efficiency, and so on. Thus, the compound approach enables practical applications in next-generation cameras and sensors. Furthermore, we explore the advancement of cascaded metasurfaces in the realm of wave-optics, specifically for advanced meta-holography and optical computing. These multi-layered systems facilitate complex wavefront engineering, leading to significant increases in information capacity and functionality for security and analog optical computing applications. By providing a comprehensive overview of fundamental principles, design strategies, and emerging applications, this review aims to offer a clear perspective on the pivotal role of compound meta-optics in devising and optimizing compact, multifunctional optical systems to optics engineers with a variety of professional knowledge backgrounds and techniques. Full article

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss; anti-vascular endothelial growth factor (anti-VEGF) therapy is standard care for neovascular AMD (nAMD), yet treatment response varies. We systematically reviewed and meta-analyzed artificial intelligence (AI) and machine learning (ML) models using optical coherence tomography (OCT)-derived information to predict anti-VEGF treatment response in nAMD. PubMed, Embase, Web of Science, and IEEE Xplore were searched from inception to 18 December 2025 for eligible studies reporting threshold-based performance. Two reviewers screened studies, extracted data, and assessed risk of bias using PROBAST+AI; pooled sensitivity and specificity were estimated with a bivariate random-effects model. Seven studies met inclusion criteria, and six were synthesized quantitatively. Pooled sensitivity was 0.79 (95% CI 0.68–0.87), and pooled specificity was 0.83 (95% CI 0.62–0.94), with substantial heterogeneity. Specificity tended to be higher for long-term and functional outcomes than for short-term and anatomical outcomes. Most studies had a high risk of bias, mainly due to limited external validation and incomplete reporting. OCT-based AI models may help stratify treatment response in nAMD, but prospective, multicenter validation and standardized outcome definitions are needed before routine use; current evidence shows no consistent advantage of deep learning over engineered radiomic features. Full article

Understanding thrombosis in acute coronary syndromes (ACSs) has evolved through advances in biomarkers, intracoronary imaging, and emerging analytical tools, improving diagnostic accuracy and risk stratification in high-risk patients. This narrative review provides an integrative overview of contemporary evidence from clinical trials, meta-analyses, and international guidelines addressing circulating biomarkers, intracoronary imaging modalities—including optical coherence tomography (OCT), intravascular ultrasound (IVUS), and near-infrared spectroscopy (NIRS)—artificial intelligence–based analytical approaches, and emerging antithrombotic therapies. High-sensitivity cardiac troponins and natriuretic peptides remain the most robust and guideline-supported biomarkers for diagnosis and prognostic assessment in ACS, whereas inflammatory markers and multimarker strategies offer incremental prognostic information but lack definitive validation for routine therapeutic guidance. Intracoronary imaging with IVUS or OCT is supported by current guidelines to guide percutaneous coronary intervention in selected patients with ACS and complex coronary lesions, leading to improved procedural optimization and clinical outcomes compared with angiography-guided strategies. Beyond procedural guidance, OCT enables detailed plaque characterization and mechanistic insights into ACS, while NIRS provides complementary information on lipid-rich plaque burden, primarily for risk stratification based on observational evidence. Artificial intelligence represents a rapidly evolving tool for integrating clinical, laboratory, and imaging data, with promising results in retrospective and observational studies; however, its clinical application in thrombosis management remains investigational due to the lack of outcome-driven randomized trials. In the therapeutic domain, factor XI inhibitors have demonstrated favorable safety profiles with reduced bleeding and preserved antithrombotic efficacy in phase II and early phase III studies, but their definitive role in ACS management awaits confirmation in large, outcome-driven randomized trials. Overall, the integration of biomarkers, intracoronary imaging, and emerging analytical and pharmacological strategies highlights the potential for more individualized cardiovascular care. Nevertheless, careful interpretation of existing evidence, rigorous validation, and alignment with guideline-directed practice remain essential before widespread clinical adoption. Full article

Background: Maximal safe resection remains a central determinant of outcomes in glioma surgery, yet intraoperative discrimination between tumor and normal brain tissue is limited by the speed and subjectivity of frozen-section analysis. Label-free techniques such as Raman spectroscopy, mass spectrometry (MS), and optical coherence tomography (OCT) offer real-time biochemical and structural characterization that may enhance surgical precision. Their comparative diagnostic accuracy across clinically relevant endpoints has not been comprehensively evaluated. Methods: Following PRISMA 2020 guidelines, a systematic review and quantitative meta-analysis were conducted using PubMed, Embase, Scopus, and Web of Science through December 2024. Original human studies evaluating Raman, MS, or OCT for intraoperative glioma margin assessment were included. Pooled sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated using a random-effects model. Subgroup analyses addressed tumor versus normal brain tissue, infiltrated versus non-infiltrated margins, and IDH-mutant versus wild-type gliomas. Results: Twenty-four studies comprising 1768 patients met the inclusion criteria. Across all modalities, pooled sensitivity and specificity were 0.89 (95% CI 0.86–0.92) and 0.88 (95% CI 0.84–0.91), with a pooled DOR of 65.7 (95% CI 42.3–101.8; logDOR 4.18), indicating high overall discriminative performance. Tumor versus normal differentiation achieved DOR 72.4 (logDOR 4.28; I² = 26%), infiltrated margin detection DOR 41.8 (logDOR 3.73; I² = 41%), and IDH classification DOR 52.3 (logDOR 3.96; I² = 29%). No publication bias was observed. Raman and MS outperformed OCT. Conclusions: Raman spectroscopy, mass spectrometry, and OCT demonstrate strong diagnostic accuracy for real-time intraoperative glioma evaluation, enabling reliable tissue differentiation and molecular profiling that may enhance resection extent and support precision, molecularly informed neurosurgery. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

Objectives: This systematic review and meta-analysis aimed to evaluate microbial adhesion and biofilm formation on additively manufactured composite-based orthodontic clear aligners compared with thermoformed aligners and other conventional polymeric materials. The influence of material composition, surface roughness, post-processing parameters, and cleaning protocols on microbial colonization was also assessed. Methods: A comprehensive search of PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library was conducted up to September 2025. Only in vitro studies investigating microbial adhesion, biofilm biomass, or microbiome changes on three-dimensional (3D)-printed aligner composites were included. Primary outcomes consisted of colony-forming units (CFU), optical density (OD) from crystal violet assays, viable microbial counts, and surface roughness. Risk of bias was assessed using the RoBDEMAT tool. Data were narratively synthesized, and a random-effects meta-analysis was performed for comparable datasets. Results: Five studies fulfilled the inclusion criteria, of which two in vitro studies were eligible for meta-analysis. Microbial adhesion and biofilm accumulation were influenced by the manufacturing technique, composite resin formulation, and surface characteristics. Certain additively manufactured aligners exhibited smoother surfaces and reduced bacterial adhesion compared with thermoformed controls, whereas others with increased surface roughness showed higher biofilm accumulation. Incorporating bioactive additives such as chitosan nanoparticles reduced Streptococcus mutans biofilm formation without compromising material properties. The meta-analysis, based on two in vitro studies, demonstrated higher OD values for bacterial biofilm on 3D-printed aligners compared with thermoformed aligners, indicating increased biofilm biomass (p < 0.05), but not necessarily viable bacterial load. Conclusions: Microbial adhesion and biofilm formation on 3D-printed composite clear aligners are governed by resin composition, additive manufacturing parameters, post-curing processes, and surface finishing. Although certain 3D-printed materials display antibacterial potential, the limited number of studies restricts the generalizability of these findings. Clinical Significance: Optimizing composite formulations for 3D printing, alongside careful post-curing and surface finishing, may help reduce microbial colonization. Further research is required before translating these findings into definitive clinical recommendations for clear aligner therapy. Full article

Optical metasurfaces fabricated via electron beam lithography (EBL) are increasingly pivotal for biosensing and bioimaging applications. However, charge accumulation on insulating glass substrates persists as a critical barrier, causing distortion of the incident electron beam and degradation of patterning fidelity manifested as pattern deflection, increased line-edge roughness (LER), and overlay inaccuracy. Here, we evaluate three charge-mitigation strategies: optimization of electron-beam resist (EBR) thickness, spin-coated conductive polymer layers, and thin metal capping layers. A reduction in EBR thickness from 800 nm to 150 nm led to a significant improvement in LER attributed to a shortened charge dissipation path. The introduction of a conductive polymer further enhanced pattern integrity, whereas the most substantial improvement was attained by depositing a 20 nm Au layer, which offers a highly conductive pathway for rapid charge dissipation and resulted in the lowest LER of 0.24. Our comparison establishes a clear hierarchy of effectiveness and identifies metal capping as the most reliable approach for high-fidelity nanofabrication on insulating substrates, thereby offering practical solutions for advancing glass-based photonic and meta-optical devices. Full article

Background and Objectives: Corneal opacity is the fifth global cause of blindness and moderate-to-severe visual impairment due to scar tissue formation. The purpose of this study is to provide an integrated overview of the current state of corneal engineering strategies focused on the comparison with healthy corneas. It aims to identify engineering strategies that would result in functional corneas, providing real alternatives to donor corneal transplants. Materials and Methods: systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and according to the protocol with the ID: CRD420250654641 at the PROSPERO database. The focus question, prompted by considering the shortage of human corneal grafts, was: what is the performance of bioengineered corneal grafts in experimental animal models when compared with healthy eyes in the restoration of corneal anatomy and function? Results: Incorporating human corneal epithelial cells w/ or w/o human corneal stromal stem cells into a gelatin methacrylate and polyethylene glycol diacrylate matrix emerges as the leading option for epithelial layer regeneration. Human and bovine decellularized corneas, porcine corneal ECM in Gelatin methacrylate, dual layered collagen vitrigel and tissue-engineered human anterior hemi-corneas have shown promise for simultaneous regeneration of the corneal stromal and epithelial layers. Corneal stromal tissue regeneration could be positively impacted by transplantation with grafts derived from aligned self-lifting analogous tissue equivalents and collagen-based hydrogels. Finally, scaffolds of silk fibroin and human purified type I collagen represent promising approaches for corneal endothelial regeneration, though their effectiveness is contingent upon integration with endothelial cells. Conclusions: Collectively, these findings contribute to the growing body of evidence supporting the potential of tissue-engineered corneal substitutes as viable therapeutic options for corneal blindness and vision impairment. Assessing the optical and functional properties of the regenerated cornea should be a cornerstone in all studies aiming to evaluate their clinical effectiveness. Full article

Percutaneous coronary intervention (PCI) has evolved significantly over the past two decades, yet challenges in achieving optimal stent deployment and long-term outcomes persist, particularly in complex coronary anatomy. Intravascular imaging (IVI) modalities such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) have transformed the precision of PCI by providing detailed cross-sectional visualization of vessel architecture, plaque morphology, and stent apposition. Compared to angiography-guided PCI, imaging-guided PCI enables more accurate lesion assessment, appropriate stent sizing, and detection of suboptimal results including under-expansion, malapposition, and edge dissections, factors strongly linked to restenosis and stent thrombosis. Large-scale randomized trials (e.g., ULTIMATE, ILUMIEN) and meta-analyses have demonstrated that imaging-guided PCI reduces major adverse cardiovascular events (MACE) and improves long-term stent patency, particularly in left main, bifurcation, and calcified lesions. Despite these benefits, adoption remains variable due to cost, procedural complexity, and training gaps. Emerging advances, including artificial intelligence-enhanced imaging, hybrid devices, and fusion of imaging with physiologic assessments, promise to integrate imaging more seamlessly into routine practice. This review summarizes current evidence, practical applications, and future directions of IVI-guided PCI, underscoring its growing role in contemporary interventional cardiology and its potential to personalize and optimize coronary revascularization strategies. Full article

Background: The sphenoid sinus is essential for transsphenoidal surgical accesses to the sellar and parasellar regions because of its anatomic proximity to vital vascular and neurologic structures such as the internal carotid artery, optic nerve, and cavernous sinus. The high degree of morphological variability of the sphenoid sinus has a significant impact on surgical technique and the risk of intraoperative complications. Detailed knowledge of individual anatomy is therefore crucial for the safety and efficacy of transsphenoidal approaches. Objectives: This review aims to conduct a systematic analysis of the current scientific literature on anatomical variations in the sphenoid sinus and their clinical relevance in surgical interventions to the skull base. Special attention is paid to the influence of morphological features on surgical strategies to pathological processes in this area and postoperative outcomes. Materials and Methods: A systematic review of the literature was conducted according to PRISMA 2020 guidelines. The PubMed, Scopus, Web of Science, and Google Scholar databases were searched for the period March 2010 to March 2025. Keywords such as “sphenoid sinus”, “anatomical variations”, “transsphenoidal surgery” and “skull base” were used. Original studies, systematic reviews, and meta-analyses focused on the anatomy, pneumatization, and surgical significance of sphenoid sinus variations are included. Quality and relevance criteria for published material were considered in the selection of articles. Results: The most commonly identified anatomic variations included sellar and lateral pneumaticity, the presence of Onodi cells, multiple and deviated septa, and dehiscence of the posterior wall of the sphenoid sinus and prolapse into its cavity of the internal carotid artery. These variations are associated with an increased risk of intraoperative vascular injury, visual deficit, and postoperative liquorrhea. Accurate preoperative assessment by high-resolution computed axial tomography and magnetic resonance imaging, as well as the use of intraoperative neuronavigation, are critical to reduce surgical risk. Conclusions: Anatomic variations in the sphenoid sinus are an essential factor to consider when planning and performing transsphenoidal surgical accesses. An individualized approach based on detailed diagnostic imaging analysis and neuronavigation technologies contributes to a higher safety of the performed surgical interventions, a better radicality of tumor resection and more favorable postoperative outcomes. Full article

This paper provides an overview of artificial intelligence (AI) applications in ophthalmology, with a focus on diagnosing dry eye disease (DED). We aim to synthesize studies that explicitly compare AI-based diagnostic models with clinical tests employed by ophthalmologists, examine results obtained using similar imaging modalities, and identify recurring limitations to propose recommendations for future work. We conducted a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across four databases: Google Scholar, PubMed, ScienceDirect, and the Cochrane Library. We targeted studies published between 2020 and 2025 and applied predefined inclusion criteria to select 30 original peer-reviewed articles. We then analyzed each study based on the AI models used, development strategies, diagnostic performance, correlation with clinical parameters, and reported limitations. The imaging modalities covered include videokeratography, smartphone-based imaging, tear film interferometry, anterior segment optical coherence tomography, infrared meibography, in vivo confocal microscopy, and slit-lamp photography. Across modalities, deep learning models (e.g., U-shaped Convolutional Network (U-Net), Residual Network (ResNet), Densely Connected Convolutional Network (DenseNet), Generative Adversarial Networks (GANs), transformers) demonstrated promising performance, often matching or surpassing clinical assessments, with reported accuracies ranging from 82% to 99%. However, few studies performed external validations or addressed inter-expert variability. The findings confirm AI’s potential in DED diagnosis, but emphasize gaps in data diversity, clinical use, and reproducibility. It offers practical recommendations for future research to bridge these gaps and support AI deployment in routine eye care. Full article

Epiretinal membrane (ERM) is a common retinal pathology associated with progressive visual impairment, requiring timely and accurate assessment. Recent advances in artificial intelligence (AI) have enabled automated approaches for ERM detection, segmentation, and postoperative best corrected visual acuity (BCVA) prediction, offering promising avenues to enhance clinical efficiency and diagnostic precision. We conducted a comprehensive literature search across MEDLINE (via PubMed), Scopus, CENTRAL, ClinicalTrials.gov, and Google Scholar from the inception to 31 December 2023. A total of 42 studies were included in the systematic review, with 16 eligible for meta-analysis. Risk of bias and reporting quality were assessed using the QUADAS-2 and CLAIM tools. Meta-analysis of 16 studies (533,674 images) showed that deep learning (DL) models achieved high diagnostic accuracy (AUC = 0.97), with pooled sensitivity and specificity of 0.93 and 0.97, respectively. Optical coherence tomography (OCT)-based models outperformed fundus-based ones, and although performance remained high under external validation, the positive predictive value (PPV) declined—highlighting the importance of testing model generalizability. To the best of our knowledge, this is the first systematic review and meta-analysis to critically evaluate the role of AI in the detection, segmentation, and postoperative BCVA prediction of ERM across various ophthalmic imaging modalities. Our findings provide a clear overview of current evidence supporting the continued development and clinical adoption of AI tools for ERM diagnosis and management. Full article

Conventional pixel-wise satellite-derived bathymetry (SDB) models face dual challenges: physical ambiguity from variable water quality and spatial incoherence from ignoring geographic context. This study addresses these limitations by proposing and validating OptiFusionStack, a novel two-stage physio-spatial synergistic framework that operates without in situ optical data for model calibration. The framework first generates diverse, physics-informed predictions by integrating Quasi-Analytical Algorithm (QAA)-derived inherent optical properties (IOPs) with multiple base learners. Critically, it then constructs a multi-scale spatial context by computing neighborhood statistics over an experimentally optimized 9 × 9-pixel window. These physical priors and spatial features are then effectively fused by a StackingMLP meta-learner. Validation in optically diverse environments demonstrates that OptiFusionStack significantly surpasses the performance plateau of pixel-wise methods, elevating inversion accuracy (e.g., R² elevated from 0.66 to >0.92 in optically complex inland waters). More importantly, the framework substantially reduces spatial artifacts, producing bathymetric maps with superior spatial coherence. A rigorous benchmark against several state-of-the-art, end-to-end deep learning models further confirms the superior performance of our proposed hierarchical fusion architecture in terms of accuracy. This research offers a robust and generalizable new approach for high-fidelity geospatial modeling, particularly under the common real-world constraint of having no in situ data for optical model calibration. Full article

Background and Objectives: Dermoscopy is a non-invasive clinical tool that allows for the in vivo visualization of pigmented and non-pigmented structures in the epidermis and the papillary dermis. The standard handheld dermoscopy offers a magnification of 10×, whereas the videodermatoscopes can obtain a magnification of up to 140×. Recently, a new method called magnified dermoscopy was introduced, in which a magnification of 400× can be achieved. Materials and Methods: This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Comprehensive research was conducted using the PubMed database on 9 June 2025, using the following keywords: “high magnification” or “super high magnification” or “optical super high magnification” or “400×”, and “dermoscopy” or “dermatoscopy”. Results: From a total of 237 records retrieved, 25 were found to be suitable for this review, and consisted of: four prospective studies, three retrospective studies, six case series, ten case reports and two image letters. Conclusions: This review summarizes the current knowledge on magnified dermoscopy, compiling existing data and exploring future perspectives for this emerging non-invasive diagnostic method. Full article