Search Results (328)

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Objectives: Pelvic tumor resections demand high surgical precision to ensure clear margins while preserving function. Although patient-specific instruments (PSIs) improve osteotomy accuracy, positioning errors remain a limitation. This study evaluates the feasibility, accuracy, and usability of a novel dual-function augmented reality (AR) system for intraoperative guidance in PSI positioning and osteotomy execution using a head-mounted display (HMD). The system provides dual-function support by assisting both PSI placement and osteotomy execution. Methods: Ten fresh-frozen cadaveric hemipelves underwent AR-assisted internal hemipelvectomy, using customized 3D-printed PSIs and a new in-house AR software integrated into an HMD. Angular and translational deviations between planned and executed osteotomies were measured using postoperative CT analysis. Absolute angular errors were computed from plane normals; translational deviation was assessed as maximum error at the osteotomy corner point in both sagittal (pitch) and coronal (roll) planes. A Wilcoxon signed-rank test and Bland–Altman plots were used to assess intra-workflow cumulative error. Results: The mean absolute angular deviation was 5.11 ± 1.43°, with 86.66% of osteotomies within acceptable thresholds. Maximum pitch and roll deviations were 4.53 ± 1.32 mm and 2.79 ± 0.72 mm, respectively, with 93.33% and 100% of osteotomies meeting translational accuracy criteria. Wilcoxon analysis showed significantly lower angular error when comparing final executed planes to intermediate AR-displayed planes (p < 0.05), supporting improved PSI positioning accuracy with AR guidance. Surgeons rated the system highly (mean satisfaction ≥ 4.0) for usability and clinical utility. Conclusions: This cadaveric study confirms the feasibility and precision of an HMD-based AR system for PSI-guided pelvic osteotomies. The system demonstrated strong accuracy and high surgeon acceptance, highlighting its potential for clinical adoption in complex oncologic procedures. Full article

With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety. Full article

7,12-Dimethylbenzo[a]anthracene (DMBA-7,12), a highly toxic and environmentally persistent polycyclic aromatic hydrocarbon (PAH), poses significant threats to marine biodiversity and human health due to its bioaccumulation through the food chain. Conventional chromatographic methods, while achieving comparable detection limits, are hindered by the need for expensive instrumentation and prolonged analysis times, rendering them unsuitable for rapid on-site monitoring of DMBA-7,12 in marine environments. Therefore, the development of novel, efficient detection techniques is imperative. In this study, we have successfully developed an electrochemical immunosensor based on a polydopamine (PDA)–chitosan (CTs) composite interface to overcome existing technical limitations. PDA provides a robust scaffold for antibody immobilization due to its strong adhesive properties, while CTs enhances signal amplification and biocompatibility. The synergistic integration of these materials combines the high efficiency of electrochemical detection with the specificity of antigen–antibody recognition, enabling precise qualitative and quantitative analysis of the target analyte through monitoring changes in the electrochemical properties at the electrode surface. By systematically optimizing key experimental parameters, including buffer pH, probe concentration, and antibody loading, we have constructed the first electrochemical immunosensor for detecting DMBA-7,12 in seawater. The sensor achieved a detection limit as low as 0.42 ng/mL. In spiked seawater samples, the recovery rates ranged from 95.53% to 99.44%, with relative standard deviations (RSDs) ≤ 4.6%, demonstrating excellent accuracy and reliability. This innovative approach offers a cost-effective and efficient solution for the in situ rapid monitoring of trace carcinogens in marine environments, potentially advancing the field of marine pollutant detection technologies. Full article

Background/Objectives: Concurrent outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A and B viruses (IAV/IBV), and respiratory syncytial virus (RSV) necessitate rapid and precise differential laboratory diagnostic methods. This study aimed to evaluate the multiplex molecular diagnostic performance of the geneLEAD VIII system (Precision System Science Co., Ltd., Matsudo, Japan), a fully automated sample-to-result precision instrument, in conjunction with the VIASURE SARS-CoV-2, Flu & RSV Real Time PCR Detection Kit (CerTest Biotec, S.L., Zaragoza, Spain). Methods: The specific detection capabilities of SARS-CoV-2, IAV/IBV, and RSV genes were evaluated using virus-spiked saliva and nasal swab samples. Using saliva samples, the viral titer detection limits of geneLEAD/VIASURE and manual referent singleplex RT-qPCR assays were compared. The performance of geneLEAD/VIASURE in analyzing single- and multiple-infection models was scrutinized. The concordance between the geneLEAD/VIASURE and the manual assays was assessed. Results: The geneLEAD/VIASURE successfully detected all the virus genes in the saliva and nasal swab samples despite some differences in the Ct values. The viral titer detection limits in the saliva samples for SARS-CoV-2, IAV, IBV, and RSV using geneLEAD/VIASURE were 10⁰, ≤10⁻², 10⁰, and 10² TCID₅₀/mL, respectively, compared to ≤10⁻¹, ≤10⁰, ≤10⁰, and ≤10⁴ TCID₅₀/mL, respectively, in the manual assays. geneLEAD/VIASURE yielded similar Ct values in the single- and multiple-infection models, with some exceptions noted in the triple-infection models when low titers of RSV were spiked with high titers of the other viruses. The concordance between geneLEAD/VIASURE and the manual assays was high, with Pearson’s R² values of 0.90, 0.85, 0.92, and 0.95 for SARS-CoV-2, IAV, IBV, and RSV, respectively. Conclusions: geneLEAD/VIASURE is a reliable diagnostic tool for detecting SARS-CoV-2, IAV/IBV, and RSV in single- and multiple-infection scenarios. Full article

Background/Objectives: Recent breakthroughs in three-dimensional (3D) printing and high-resolution imaging have opened up new possibilities in personalized medicine, surgical planning, and forensic reconstruction. This study breaks new ground by evaluating the integration of high-resolution peripheral quantitative computed tomography (HR-pQCT) with multimodal imaging and additive manufacturing to assess a chronic, infected gunshot injury in the knee joint of a red deer. This unique approach serves as a translational model for complex skeletal trauma. Methods: Multimodal imaging—including clinical CT, MRI, and HR-pQCT—was used to characterise the extent of osseous and soft tissue damage. Histopathological and molecular analyses were performed to confirm the infectious agent. HR-pQCT datasets were segmented and processed for 3D printing using PolyJet, stereolithography (SLA), and fused deposition modelling (FDM). Printed models were quantitatively benchmarked through 3D surface deviation analysis. Results: Imaging revealed comminuted fractures, cortical and trabecular degradation, and soft tissue involvement, consistent with chronic osteomyelitis. Sphingomonas sp., a bacterium that forms biofilms, was identified as the pathogen. Among the printing methods, PolyJet and SLA demonstrated the highest anatomical accuracy, whereas FDM exhibited greater geometric deviation. Conclusions: HR-pQCT-guided 3D printing provides a powerful tool for the anatomical visualisation and quantitative assessment of complex bone pathology. This approach not only enhances diagnostic precision but also supports applications in surgical rehearsal and forensic analysis. It illustrates the potential of digital imaging and additive manufacturing to advance orthopaedic and trauma care, inspiring future research and applications in the field. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Accurate liver segmentation and volume estimation from CT images are critical for diagnosis, surgical planning, and treatment monitoring. This paper proposes a GPU-accelerated voxel-level cropping method that localizes the liver region in a single pass, significantly reducing unnecessary computation and memory transfers. We integrate this pre-processing step into two segmentation pipelines: a traditional Chan-Vese model and a deep learning U-Net trained on the LiTS dataset. After segmentation, a seeded region growing algorithm is used for 3D liver volume assessment. Our method reduces unnecessary image data by an average of 90%, speeds up segmentation by 1.39× for Chan-Vese, and improves dice scores from 0.938 to 0.960. When integrated into U-Net pipelines, the post-processed dice score rises drastically from 0.521 to 0.956. Additionally, the voxel-based cropping approach achieves a 2.29× acceleration compared to state-of-the-art slice-based methods in 3D volume assessment. Our results demonstrate high segmentation accuracy and precise volume estimates with errors below 2.5%. This proposal offers a scalable, interpretable, efficient liver segmentation and volume assessment solution. It eliminates unwanted artifacts and facilitates real-time deployment in clinical environments where transparency and resource constraints are critical. It is also tested in other anatomical structures such as skin, lungs, and vessels, enabling broader applicability in medical imaging. Full article

Background: Renal tumors, encompassing benign, malignant, and normal variants, represent a significant diagnostic challenge in radiology due to their overlapping visual characteristics on computed tomography (CT) scans. Manual interpretation is time consuming and susceptible to inter-observer variability, emphasizing the need for automated, reliable classification systems to support early and accurate diagnosis. Method and Materials: We propose KidneyNeXt, a custom convolutional neural network (CNN) architecture designed for the multi-class classification of renal tumors using CT imaging. The model integrates multi-branch convolutional pathways, grouped convolutions, and hierarchical feature extraction blocks to enhance representational capacity. Transfer learning with ImageNet 1K pretraining and fine tuning was employed to improve generalization across diverse datasets. Performance was evaluated on three CT datasets: a clinically curated retrospective dataset (3199 images), the Kaggle CT KIDNEY dataset (12,446 images), and the KAUH: Jordan dataset (7770 images). All images were preprocessed to 224 × 224 resolution without data augmentation and split into training, validation, and test subsets. Results: Across all datasets, KidneyNeXt demonstrated outstanding classification performance. On the clinical dataset, the model achieved 99.76% accuracy and a macro-averaged F1 score of 99.71%. On the Kaggle CT KIDNEY dataset, it reached 99.96% accuracy and a 99.94% F1 score. Finally, evaluation on the KAUH dataset yielded 99.74% accuracy and a 99.72% F1 score. The model showed strong robustness against class imbalance and inter-class similarity, with minimal misclassification rates and stable learning dynamics throughout training. Conclusions: The KidneyNeXt architecture offers a lightweight yet highly effective solution for the classification of renal tumors from CT images. Its consistently high performance across multiple datasets highlights its potential for real-world clinical deployment as a reliable decision support tool. Future work may explore the integration of clinical metadata and multimodal imaging to further enhance diagnostic precision and interpretability. Additionally, interpretability was addressed using Grad-CAM visualizations, which provided class-specific attention maps to highlight the regions contributing to the model’s predictions. Full article

Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on a deep learning framework. A semantic segmentation network called SCTNet is employed to perform high-precision semantic segmentation, while a sliding window strategy is introduced to address the challenges associated with large-scale image processing during training and inference. The proposed method achieves a mean Intersection over Union (mIoU) of 72.14% and a pixel-level segmentation accuracy of 97% on the test dataset, outperforming traditional thresholding techniques and several state-of-the-art deep learning models. Besides fracture detection, the method enables quantitative characterization of fracture-related parameters, including fracture proportion, dip angle, strike, and aperture. Experimental results indicate that the proposed approach provides a reliable and efficient solution for the interpretation of large-volume CT data. Compared to manual evaluation, the method significantly accelerates the analysis process—reducing time from hours to minutes—and demonstrates strong potential to enhance intelligent workflows for geological core fracture analysis. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Background/Objectives: Limb malalignment disrupts physiological joint forces and predisposes individuals to the development of osteoarthritis. Surgical interventions such as distal femur or high tibial osteotomy aim to restore mechanical balance on weight-bearing joints, thereby reducing long-term morbidity. Accurate alignment is crucial since it cannot be adjusted after stabilization with plates and screws. Recent advances in personalized medicine offer the opportunity to tailor surgical corrections to each patient’s unique anatomy and biomechanical profile. This study evaluates the benefits of 3D planning and patient-specific cutting guides over traditional 2D planning with standard implants for alignment correction procedures. Methods: We assessed limb alignment parameters pre- and postoperatively in patients with varus and valgus lower limb malalignment undergoing acute realignment surgery. The cohort included 23 opening-wedge high tibial osteotomies and 28 opening-wedge distal femur osteotomies. We compared the accuracy of postoperative alignment parameters between patients undergoing traditional 2D preoperative X-ray planning and those using 3D reconstructions of CT data. Outcome measures included mechanical axis deviation and tibiofemoral angles. Results: 3D reconstructions of computerized tomography data and patient-specific cutting guides significantly reduced the variation in postoperative limb alignment parameters relative to preoperative goals. In contrast, traditional 2D planning with standard non-custom implants resulted in higher deviations from the targeted alignment. Conclusions: Utilizing 3D CT reconstructions and patient-specific cutting guides enhances the accuracy of postoperative limb realignment compared to traditional 2D X-ray planning with standard non-custom implants. Patient-specific instrumentation and personalized approaches represent a key step toward precision orthopedic surgery, tailoring correction strategies to individual patient anatomy and potentially improving long-term joint health. This improvement may reduce the morbidity associated with lower limb malalignment and delay the onset of osteoarthritis. Level of Evidence: Therapeutic Level III. Full article

Tricuspid regurgitation (TR) represents a significant, often silently progressing, valvular heart disease with historically suboptimal management due to perceived high surgical risks. Transcatheter tricuspid valve interventions (TTVI) offer a promising, less invasive therapeutic avenue. Central to the success of TTVI is Right Ventricular Reverse Remodelling (RVRR), defined as an improvement in RV structure and function, which strongly correlates with enhanced patient survival. The right ventricle (RV) undergoes complex multi-scale biomechanical maladaptations, progressing from adaptive concentric to maladaptive eccentric hypertrophy, coupled with increased stiffness and fibrosis. Molecular drivers of this pathology include early failure of antioxidant defenses, metabolic shifts towards glycolysis, and dysregulation of microRNAs. Accurate RV function assessment necessitates advanced imaging modalities like 3D echocardiography, Cardiac Magnetic Resonance Imaging (CMR), and Computed Tomography (CT), along with strain analysis. Following TTVI, RVRR typically manifests as a biphasic reduction in RV volume overload, improved myocardial strain, and enhanced RV-pulmonary arterial coupling. Emerging molecular biomarkers alongside advanced imaging-derived biomechanical markers like CT-based 3D-TAPSE and RV longitudinal strain, are proving valuable. Artificial intelligence (AI) and machine learning (ML) are transforming prognostication by integrating diverse clinical, laboratory, and multi-modal imaging data, enabling unprecedented precision in risk stratification and optimizing TTVI strategies. Full article

Background: Delay in diagnosing metastatic epidural spinal cord compression (MESCC) adversely impacts clinical outcomes. High-grade MESCC is frequently overlooked on routine staging CT scans. We aim to assess the potential of our deep learning model (DLM) in detecting high-grade MESCC and reducing diagnostic delays. Methods: This retrospective review analyzed 140 patients with surgically treated MESCC between C7 and L2 during 2015–2022. An experienced radiologist (serving as the reference standard), a consultant spine surgeon, and the DLM independently classified staging CT scans into high-grade MESCC or not. The findings were compared to original radiologist (OR) reports; inter-rater agreement was assessed. Diagnostic delay referred to the number of days elapsed from CT to diagnostic MRI scan. Results: Overall, 95/140 (67.8%) patients had preoperative CT scans. High-grade MESCC was identified in 84/95 (88.4%) of the scans by the radiologist (reference standard), but in only 32/95 (33.7%) of the preoperative scans reported by the OR. There was almost perfect agreement between the radiologist and the surgeon (kappa = 0.947, 95% CI = 0.893–1.000) (p < 0.001), and between the radiologist and the DLM (kappa = 0.891, 95% CI = 0.816–0.967) (p < 0.001). In contrast, inter-observer agreement between the OR and all other readers was slight (kappa range = 0.022–0.125). Diagnostic delay was potentially reduced by 20 ± 28 (range = 1–131) days. Conclusions: The original radiologist reports frequently missed high-grade MESCC in staging CT. Our DLM for CT diagnosis of high-grade MESCC showed almost perfect inter-rater agreement with two experienced reviewers. This study is the first to demonstrate that the DLM could help reduce diagnostic delays. Further prospective research is required to understand its precise role in improving the early diagnosis/treatment of MESCC. Full article

Background: BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. Methods: This systematic review explores the integration of AI techniques—particularly DL and CNNs—with neuroimaging modalities such as MRI, CT, EEG, and ECoG for automated brain mapping and tissue classification. Eligible clinical and computational studies, primarily published between 2015 and 2025, were identified via PubMed, Scopus, and IEEE Xplore. The review follows PRISMA guidelines and is registered with the OSF (registration number: J59CY). Results: AI-based segmentation methods have demonstrated Dice similarity coefficients exceeding 0.91 in glioma boundary delineation and tumor segmentation tasks. Concurrently, BCI systems leveraging EEG and SSVEP paradigms have achieved information transfer rates surpassing 22.5 bits/min, enabling high-speed neural decoding with sub-second latency. We critically evaluate real-time neural signal processing pipelines and AI-guided surgical robotics, emphasizing clinical performance and architectural constraints. Integrated systems improve targeting precision and postoperative recovery across select neurosurgical applications. Conclusions: This review consolidates recent advancements in BCI and AI-driven medical imaging, identifies barriers to clinical adoption—including signal reliability, latency bottlenecks, and ethical uncertainties—and outlines research pathways essential for realizing closed-loop, intelligent neurosurgical platforms. Full article