Search Results (11,786)

Glomerular filtration rate (GFR) is a key indicator of renal function. Traditional methods for GFR measurement have limitations including invasiveness, low spatial resolution, and lengthy protocols. Positron emission tomography (PET) radiotracers have emerged as promising tools for non-invasive, accurate, and dynamic renal function assessment. Objectives: This systematic literature review evaluates the clinical utility, and current evidence surrounding PET radiotracers used for GFR measurement in humans, emphasizing advances over conventional renal imaging modalities. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, from database inception to November 2024. The search identified studies evaluating PET-based measurement of glomerular filtration rate (GFR) and renal perfusion. Inclusion criteria encompassed human studies using PET radiotracers (e.g., ⁶⁸Ga, ¹⁸F) with comparisons to reference standards (estimated GFR or serum creatinine). Two authors independently screened titles/abstracts, extracted data, and assessed bias using Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). Exclusions included animal studies, reviews, and non-English articles. Results: Eleven studies met inclusion criteria, with ⁶⁸Ga-EDTA showing the highest validation against reference standards such as ⁵¹Cr-EDTA plasma clearance, demonstrating strong correlation. PET imaging offered superior spatial–temporal resolution, enabling accurate split renal function assessment and quantitative analysis of both filtration and perfusion. ⁶⁸Ga-somatostatin analogues exhibited moderate correlations between renal SUV and estimated GFR, with post-PRRT uptake changes indicating early nephrotoxicity. Among novel tracers, ⁶⁸Ga-FAPI showed a strong inverse SUV–GFR relationship, reflecting renal fibrosis and suggesting potential as a chronic kidney disease (CKD) biomarker but requires further clinical validation. Limitations across studies include small sample sizes, retrospective designs, and variability in reference standards. Conclusions: PET radiotracers, particularly ⁶⁸Ga-EDTA, represent a significant advancement for non-invasive, quantitative GFR measurement with improved precision and renal anatomical detail compared to traditional methods. Future prospective, large-scale human studies with standardized protocols are needed to establish these PET tracers as routine clinical tools in nephrology. Integration of hybrid PET/MRI and novel tracer development may further enhance renal diagnostic capabilities. Full article

Background: Nanomedicine has demonstrated great potential to improve drug delivery across various diseases. However, accurately monitoring the real-time trafficking of organic nanoparticles (NPs) within biological systems remains a significant challenge. Current detection methods rely heavily on fluorescence, while high-resolution, label-free imaging is often precluded by the limited optical contrast of organic materials, limiting a comprehensive understanding of NP fate. Metallic doping allows simultaneous detection of carriers using multiple imaging and analysis techniques. This study presents a novel approach to prepare gold-doped hybrid NPs compatible with multimodal imaging, thus facilitating multimodal tracking. Methods: Gold-doped NPs were successfully synthesized via nanoprecipitation, yielding stable, monodisperse carriers with optimal size, confirmed by Dynamic Light Scattering and Nanoparticle Tracking Analysis. UV/Vis spectroscopy confirmed effective gold-doping, with doping efficiency of approximately 50%. Transmission Electron Microscopy (TEM) showed gold NP accumulation throughout the polymer core and near the lipid shell. Results: Although gold doping resulted in a slight increase in NP size and zeta potential, no effects on cytocompatibility or cellular uptake by glioblastoma and microglia cells were observed. Furthermore, the optical properties (i.e., the refractive index and the UV spectrum) of the NPs were successfully modified to enable tracking across complementary imaging modalities. Real-time, label-free visualization of NP accumulation in the cytoplasm of U87 cells was achieved via holotomography by exploiting the enhanced refractive index after gold-doping. This observation was confirmed through correlation with fluorescence confocal microscopy, using fluorescently labelled gold-doped NPs. Furthermore, the high electron density of the gold tracer facilitated the precise localization of NPs within intracellular compartments via TEM, bypassing the inherently low contrast of organic NPs. Conclusions: These findings validated the gold-doped NPs as versatile nanoplatforms for multimodal imaging, showcasing their potential for non-invasive, high-resolution tracking and more accurate quantification of intracellular accumulation using diverse analytical techniques. Full article

Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental treatment features on CBCT images at both tooth and full-scan levels. Methods: In this retrospective single-center study, 147 CBCT scans (4704 tooth positions) were analyzed. Two experienced readers annotated treatment features (missing teeth, fillings, endodontic treatments, crowns, pontics, orthodontic appliances, implants), and consensus served as the reference. Anonymized datasets were processed by a cloud-based AI system (Diagnocat Inc., San Francisco, CA, USA). Diagnostic metrics—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score—were calculated with 95% patient-clustered bootstrap confidence intervals. A “Perfect Agreement” criterion defined full-scan level success as an entirely error-free full-mouth report. Results: Tooth-level AI performance was excellent, with accuracy exceeding 99% for most categories. Sensitivity was highest for missing teeth (99.3%) and endodontic treatments (99.0%). Specificity and NPV exceeded 98.5% and 99.7%, respectively. Full-scan level Perfect Agreement was achieved in 82.3% (95% CI: 76.2–88.4%), with errors concentrated in teeth presenting multiple co-existing findings. Conclusions: The evaluated AI platform demonstrates near-perfect accuracy in detecting isolated dental features but moderate reliability in generating complete full-mouth reports. It functions best as an assistive diagnostic tool, not as an autonomous system. Full article

COVID-19 screening is crucial for the early diagnosis and treatment of the disease, with lung ultrasound posing as a cost-effective alternative to other imaging techniques. Given the dependency on medical expertise and experience to accurately identify patterns in ultrasound exams, deep learning techniques have been explored for automatically classifying patients’ conditions. However, the limited availability of public medical databases remains a significant obstacle to the development of more advanced models. To address the data scarcity problem, this study proposes a method that leverages generative adversarial networks (GANs) to generate synthetic lung ultrasound images, which are subsequently used to train frame-based classification models. Two types of GANs are considered: Wasserstein GANs (WGAN) and Pix2Pix. Specific tools are used to show that the synthetic data produced present a distribution close to the original data. The classification models trained with synthetic data achieved a peak accuracy of 96.32% ± 4.17%, significantly outperforming the maximum accuracy of 82.69% ± 10.42% obtained when training only with the original data. Furthermore, the best results are comparable to, and in some cases surpass, those reported in recent related studies. Full article

This work addresses computational inefficiency in ultra-wide-area remote sensing image (RSI) object detection. Traditional homogeneous tiling strategies enforce computational symmetry by processing all image regions uniformly, ignoring the intrinsic spatial asymmetry of target distribution where target-dense coexist with vast target-sparse areas (e.g., deserts, farmlands), thereby wasting computational resources. To overcome symmetry mismatch, we propose a heat-guided adaptive blocking and dual-model collaboration (HAB-DMC) framework. First, a lightweight EfficientNetV2 classifies initial 1024 × 1024 tiles into semantic scenes (e.g., airports, forests). A target-scene relevance metric converts scene probabilities into a heatmap, identifying high-attention regions (HARs, e.g., airports) and low-attention regions (LARs, e.g., forests). HARs undergo fine-grained tiling (640 × 640 with 20% overlap) to preserve small targets, while LARs use coarse tiling (1024 × 1024) to minimize processing. Crucially, a dual-model strategy deploys: (1) a high-precision LSK-RTDETR-base detector (with Large Selective Kernel backbone) for HARs to capture multi-scale features, and (2) a streamlined LSK-RTDETR-lite detector for LARs to accelerate inference. Experiments show 23.9% faster inference on 30k-pixel images and reduction in invalid computations by 72.8% (from 50% to 13.6%) versus traditional methods, while maintaining competitive mAP (74.2%). The key innovation lies in repurposing heatmaps from localization tools to dynamic computation schedulers, enabling system-level efficiency for Ultra-Wide-Area RSIs. Full article

A new rhodamine-based fluorescent probe (RDC, rhodamine-based derivative) was rationally designed and synthesized for the highly selective, sensitive, and quantitative detection of Cu²⁺. The probe demonstrated outstanding specificity toward Cu²⁺, even in the presence of competing metal ions (e.g., Al³⁺, Fe³⁺, Cr³⁺, Na⁺, and K⁺), exhibiting negligible interference and confirming its robust anti-interference capability. A spectroscopic analysis revealed that Cu²⁺ induced spirocyclic ring cleavage, resulting in a colorless-to-pink colorimetric transition and enhancement of the yellow–green fluorescence at 590 nm. Upon addition of Cu²⁺, the fluorescence spectrum showed a linear response in the concentration range of 0.4–20 μM, with a correlation coefficient (R²) of 0.9907 and the limit of detection (LOD) calculated to be 0.12 μM. Meanwhile, Job’s plot analysis verified that the binding stoichiometry between RDC and Cu²⁺ was 1:1. The probe exhibits rapid response kinetics (<5 min) and non-destructiveness properties, enabling in vivo imaging. Under stress conditions, Cu²⁺ accumulated predominantly in root tips (its primary target tissue), with the following distribution hierarchy: root tips > maturation zone epidermis > xylem vessels > cortical cell walls. In conclusion, RDC is a well-characterized, high-performance tool with high accuracy, excellent selectivity, and superior sensitivity for plant Cu²⁺ studies, and this work opens new technical avenues for rhodamine-based probes in plant physiology, environmental toxicity monitoring, and rational design of phytoremediation strategies. Full article

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and protection. However, the existing image segmentation methods frequently fall short of delivering optimal segmentation results. To address this issue, this study introduces a novel mural image segmentation approach termed NDFNGO, which integrates a nonlinear differential learning strategy, a decay factor, and a Fractional-order adaptive learning strategy into the Northern Goshawk Optimization (NGO) algorithm to enhance segmentation performance. Firstly, the nonlinear differential learning strategy is incorporated to harness the diversity and adaptability of differential tactics, thereby augmenting the algorithm’s global exploration capabilities and effectively improving its ability to pinpoint optimal segmentation threshold regions. Secondly, drawing on the properties of nonlinear functions, a decay factor is proposed to achieve a more harmonious balance between the exploration and exploitation phases. Finally, by integrating historical individual data, the Fractional-order adaptive learning strategy is employed to reinforce the algorithm’s exploitation capabilities, thereby further refining the quality of image segmentation. Subsequently, the proposed method was evaluated through tests on twelve mural image segmentation tasks. The results indicate that the NDFNGO algorithm achieves victory rates of 95.85%, 97.9%, 97.9%, and 95.8% in terms of the fitness function metric, PSNR metric, SSIM metric, and FSIM metric, respectively. These findings demonstrate the algorithm’s high performance in mural image segmentation, as it retains a significant amount of original image information, thereby underscoring the superiority of the technology proposed in this study for addressing this challenge. Full article

Background/Objectives: Retirement from elite sport often disrupts structured routines and performance-driven nutrition habits, leaving athletes vulnerable to maladaptive eating behaviours and body image concerns. This study aimed to explore the experiences of former elite athletes regarding healthy eating after retirement, focusing on preparedness, barriers, and enablers during the transition to post-sport life. Methods: A qualitative design was employed using semi-structured interviews with former Australian athletes (national, international, or Olympic level) recruited via snowball sampling and professional networks. Interviews were recorded, transcribed, and analysed using an inductive thematic analysis framework to identify key themes and subthemes. Results: Sixteen elite or highly trained athletes (56% female) were interviewed. Four overarching themes were apparent: (1) navigating life beyond elite sport, (2) detaching from sporting culture and belief systems, (3) reframing food, body, and control, and (4) the journey to healthy behaviours and food freedom. Participants reported identity loss, inadequate transition support, and persistent body image concerns. Over time, many described a gradual shift towards intuitive eating and improved relationships with food and self, though residual “food noise” and restrictive tendencies persisted for some. Conclusions: The findings highlight the need for athlete-centred dietetic and psychological interventions across the athletic lifecycle and post-retirement. Culture change within elite sport and the development of tailored, accessible transition resources that include digital and AI-supported tools may facilitate healthier eating behaviours and long-term wellbeing. Full article

This retrospective study evaluated the clinical utility of Virtual Reality (VR) in visualizing extracardiac CHD (eCHD) abnormalities involving great vessels, pericardium, or structures outside the heart in nine pediatric patients. Anonymized computed tomography angiography (CTA) DICOM images were processed using Elucis (Version 1.10 elucis next) software to generate interactive 3D models via segmentation. VR models were reviewed for a variety of cases: vascular rings (two with right aortic arch, aberrant left subclavian artery, and diverticulum of Kommerell; two with double aortic arch), pericardial teratomas (n = 2), right superior vena cava draining into the left atrium (n = 1), left pulmonary artery sling (n = 1), and aortopulmonary window (n = 1). VR video images were presented during weekly heart center conferences. A survey conducted among heart center staff assessed the perceived value of VR in clinical practice. A total of 62% found traditional diagnostic modalities very effective, 100% considered VR a valuable diagnostic tool, 65% responded positively to VR image resolution, 50% highlighted its educational benefit, 81% believed VR enhanced diagnostic accuracy and surgical planning, and 100% would recommend its use to colleagues. This study demonstrates the successful integration of VR-based segmentation into clinical workflows, underlining its potential as both an educational resource and a tool to support diagnostic and surgical decision-making. Full article

Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows. Full article

Background: Coronary computed tomography angiography (CCTA) is a non-invasive imaging tool used predominantly in suspected chronic coronary artery disease (CAD) patients, due to its high negative predictive value. However, increasing focus has been placed on CCTA to manage and risk stratify acute chest pain patients in emergency departments (ED). Objective: This scoping review summarizes the available evidence on the role of CCTA to exclude acute coronary syndrome (ACS) in low-risk acute chest pain patients, focusing on its diagnostic accuracy, safety, and application in the context of high sensitivity cardiac troponin assays (hs-cTn). Methods: Articles published between January 2015 and March 2025 investigating CCTA use in low-risk acute chest pain patients were retrieved from Medline, Embase, Emcare, and Web of Science databases. Results: 22 articles (13,617 patients) were retrieved. CCTA had strong diagnostic performance, with an excellent negative predictive value (99.8–100%) and sensitivity (94–100%) for ACS diagnosis and prediction of major adverse cardiovascular events. Specificity and positive predictive values were lower and less consistent. When combined with hs-cTn, the diagnostic accuracy of CCTA for ACS was improved significantly. CCTA was associated with low rates of ACS at follow-up (0–3.5%), which were lower than or comparable to the safety outcomes of standard care and stress testing. Full article

Background/Objectives: Differentiating atypical lipomatous tumours (ALT) from lipomas remains challenging, as both share similar clinical and radiological features but require different forms of management. We previously proposed a clinical–radiological score integrating routine parameters to improve preoperative discrimination. This study aimed to externally validate the score in an independent cohort and refine it for enhanced robustness. Methods: We retrospectively analysed 119 patients with lipomatous tumours treated between 2022 and 2024 at an external university hospital. Diagnostic performance of the original models was assessed using receiver operating characteristic analysis. Data were then combined with the initial development cohort (n = 106) to recalibrate the models and define new cut-offs. Results: In the external validation cohort, predictive accuracy decreased compared to the derivation cohort, especially in extremity tumours assessed without contrast (AUC 0.830 vs. 0.942). Across four recalibrated models in the combined dataset (n = 225), diagnostic accuracy remained high (AUCs 0.918–0.954). Models combining clinical and imaging parameters consistently outperformed single-parameter approaches, with contrast enhancement providing the greatest incremental value. Accuracy was lower in trunk-localised tumours, highlighting the need for molecular confirmation in selected subgroups. Conclusions: The re-modelled score demonstrated robust diagnostic accuracy and practicality for routine use, offering a resource-efficient tool to support preoperative risk stratification. While molecular testing remains essential in high-risk cases, the refined score may reduce unnecessary testing and facilitate tailored diagnostic strategies. To support clinical adoption, the score is available as a web application that automatically selects the appropriate model and presents results in a colour-coded format. Full article

Background: Recent trials, including JCOG0802/WJOG4607L and CALGB140503, have confirmed the oncological adequacy of segmentectomy for early-stage non-small-cell lung cancer (NSCLC). This shift emphasizes the preservation of pulmonary function and minimal invasiveness. Robot-assisted thoracic surgery (RATS) offers enhanced anatomical precision and potentially improves segmentectomy outcomes. Methods: We reviewed the current evidence comparing sublobar resection and lobectomy for early-stage NSCLC, focusing on RATS segmentectomy. Clinical trials, perioperative and long-term outcomes, technical innovations, and patient selection criteria were analyzed. Comparative data among RATS, video-assisted thoracoscopic surgery (VATS), and open approaches were synthesized, including the emerging roles of AI and 3D imaging. Results: Segmentectomy yields survival outcomes equivalent or superior to lobectomy for stage IA peripheral NSCLC ≤2 cm, with better pulmonary function despite higher locoregional recurrence. RATS enhances visualization, dexterity, and ergonomics, thereby enabling precise dissection and lymph node assessment. Compared to VATS and open surgery, RATS shows lower conversion rates, reduced pain, and comparable oncological control. Innovations, such as indocyanine green imaging, 3D modeling, and AI-guided navigation, support margin accuracy and personalized care. Conclusions: Segmentectomy has redefined the surgical standards for early-stage NSCLC. RATS maximizes the minimally invasive benefits by combining oncological safety and functional preservation. Its technical precision facilitates complex resections and integration with digital planning tools to advance personalized thoracic surgery. RATS represents the next evolution of minimally invasive thoracic surgery, redefining the balance between oncological safety and functional preservation in early-stage NSCLC. Full article

Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data have made traditional analysis methods difficult to scale. Artificial intelligence (AI) has emerged as a practical tool to address these challenges by providing new methods for interpreting and predicting biological behavior. Recent studies have demonstrated potential in several areas, including image-based analysis of cell and nuclear morphology, traction force microscopy (TFM), cell segmentation, motility analysis, and the detection of cancer biomarkers. Within this context, we review AI applications that either incorporate mechanical inputs/outputs directly or infer mechanobiologically relevant information from cellular and nuclear structure. This study summarizes progress in four key domains: AI/ML-based cell morphology studies, cancer biomarker identification, cell segmentation, and prediction of traction forces and motility. We also discuss the advantages and limitations of integrating AI/ML into mechanobiological research. Finally, we highlight future directions, including physics-informed and hybrid AI approaches, multimodal data integration, generative strategies, and opportunities for computational biophysics-aligned applications. Full article

An early and accurate detection of retinal lesions is imperative to intercept the course of sight-threatening ailments, such as Diabetic Retinopathy (DR) or Age-related Macular Degeneration (AMD). Manual expert annotation of all such lesions would take a long time and would be subject to interobserver tendencies, especially in large screening projects. This work introduces an end-to-end deep learning pipeline for automated retinal lesion segmentation, tailored to datasets without available expert pixel-level reference annotations. The approach is specifically designed for our needs. A novel multi-stage automated ground truth mask generation method, based on colour space analysis, entropy filtering and morphological operations, and creating reliable pseudo-labels from raw retinal images. These pseudo-labels then serve as the training input for a U-Net architecture, a convolutional encoder–decoder architecture for biomedical image segmentation. To address the inherent class imbalance often encountered in medical imaging, we employ and thoroughly evaluate a novel hybrid loss function combining Focal Loss and Dice Loss. The proposed pipeline was rigorously evaluated on the ‘Eye Image Dataset’ from Kaggle, achieving a state-of-the-art segmentation performance with a Dice Similarity Coefficient of 0.932, Intersection over Union (IoU) of 0.865, Precision of 0.913, and Recall of 0.897. This work demonstrates the feasibility of achieving high-quality retinal lesion segmentation even in resource-constrained environments where extensive expert annotations are unavailable, thus paving the way for more accessible and scalable ophthalmological diagnostic tools. Full article