Search Results (214)

Background: Definitive quantification of fluid spread within the paraneural sheath (PNS) but external to the epineurium during hydrorelease (HR)-like procedures is lacking. We aimed to investigate the spread of low-volume HR within the intra-PNS surrounding the sciatic, tibial, and common peroneal nerves using human cadaveric specimens. Methods: HR with 2.5 mL of dye-mixed saline was performed under ultrasound guidance into the intra-PNS of seven lower limbs from four fresh-frozen cadavers. Dye spread was quantified by measuring longitudinal distance and circumferential dispersion, followed by anatomical dissection within 1 min of injection. Results: All injections demonstrated consistent longitudinal spread along the intra-PNS layer without intraneural infiltration. The mean spread distances were 10.63 ± 3.66, 9.97 ± 3.60, and 8.36 ± 3.04 cm in the sciatic, tibial, and common peroneal nerves, respectively, indicating no significant differences. An opposite-side circumferential spread was observed in all cases, with mean scores indicating mild-to-moderate extension. Conclusions: Low-volume HR selectively spreads within the intra-PNS layer, suggesting that this anatomical layer is a structurally valid and reproducible target for perineural injection techniques. Full article

Advances in digital pathology and artificial intelligence (AI) are significantly transforming the approach to analyzing the tumor microenvironment (TME) in gastrointestinal cancers (GICs). The TME consists of tumor cells, stromal components, and immune cells. It plays a key role in disease progression, treatment response, and patient prognosis. This review discusses the most important TME biomarkers, such as tumor budding (TB), tumor-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs), with emphasis on their prognostic and predictive significance. Traditional histopathological assessment of these parameters is limited by subjectivity, intraobserver variability, and time-consuming nature. In this context, AI-based tools enable automated, quantitative, and more reproducible analysis of entire histological sections. Deep learning models allow the accurate detection and classification of structures and also analysis of their spatial organization. They provide new biological insights unavailable in routine diagnostics. The integration of imaging data with molecular and clinical information leads to the development of personalized medicine. Despite numerous advantages, the implementation of AI in clinical practice continues to face challenges related to standardization, data availability, and model interpretability. Full article

Background/Objectives: The aim of this study was to evaluate and compare the accuracy, reliability, and time efficiency of a convolutional neural network (CNN)-based deep learning model with manual annotation in the identification of soft tissue landmarks on two-dimensional (2D) facial images for orthodontic applications. Materials and Methods: Three-dimensional (3D) facial scans were obtained from 100 participants (50 females, 50 males) aged 18–25 years using the Revopoint Pop2 3D Scanner. Frontal and profile 2D images were extracted from the 3D models. Manual landmark identification was performed by a single investigator using LabelMe software, marking 22 landmarks on frontal images and 15 landmarks on profile images. A novel CNN model was developed and trained on these manually annotated images. The model’s automatic landmark identifications were compared with manual annotations in terms of positional error, identification time, and reproducibility. Results: The CNN model achieved a mean localization accuracy of 96.07%. The mean prediction error ranged from 2.3% to 4.5% across various anatomical points. Trichion, Menton, and Gonion points exhibited relatively higher error rates. The model significantly reduced the annotation time compared to manual identification (manual method: 237 s per image). Intra-observer reliability analysis demonstrated excellent agreement for manual landmarking (ICC: 0.85–0.95). The AI model provided consistent predictions for identical inputs. Conclusions: The deep learning-based model demonstrated comparable accuracy to manual landmark identification while significantly improving the annotation speed and reproducibility. These results suggest that CNN-based systems offer a promising alternative for clinical orthodontic analysis and digital workflow integration. Full article

Background/Objectives: Online adaptive radiotherapy (oART) generates plans at each fraction by exploiting AI-assisted optimization engines without explicit user control over modulation. This process challenges quality assurance since measurement-based Patient Specific Quality Assurance (PSQA) cannot be performed daily. This study aimed: (i) to characterize plan complexity in IOE-generated plans for prostate cancer using a reproducible set of PCMs, including the decomposition of inter-patient and intra-patient variability sources; (ii) to evaluate the association between PCMs and delivery accuracy within a cohort-informed SPC framework validated through leave-one-patient-out cross-validation; (iii) to investigate whether inter-fraction anatomical variations explain the observed plan complexity patterns, or whether complexity is predominantly an intrinsic signature of the AI-assisted optimizer. Methods: Twenty-one prostate cancer patients treated on a CBCT-based oART platform were retrospectively analyzed across three anatomical targets: prostatic bed (PrB), prostate (Pr), and prostate with seminal vesicles (PrSV). Six PCMs, namely MU/cGy, Modulation Complexity Score (MCS), Aperture Area Variability (AAV), Leaf Sequence Variability (LSV), Average Leaf Gap (ALG) and Plan Irregularity, were extracted. Additionally, five anatomical metrics (AMs) were computed from daily contours. Linear mixed-effects models (LMEMs) compared reference/online plans, decomposed variance via intraclass correlation coefficients (ICCs), and assessed PCM–gamma passing rate (GPR) associations. Leave-one-patient-out cross-validation (LOPO-CV) evaluated SPC threshold stability. The relationships between PCMs and AMs were investigated using LMEMs. Results: The AI-assisted optimization engine generated plans characterized by elevated monitor unit demand (average MU/cGy ≥ 6.8 ± 0.9) and narrow MLC apertures (ALG ≤ 17.7 mm ± 1.9 mm). No complexity differences emerged between offline and online-adapted plans, nor between anatomical targets. All PCMs showed significant associations with global GPR (p ≤ 0.027), though marginal R² remained low (≤ 0.122). Notably, GPR dispersion increased systematically at higher complexity values, indicating that highly modulated plans exhibit reduced delivery predictability. LOPO-CV demonstrated stable tolerance/action limits. Anatomical variations explained less than 35% of the total variance in PCMs. Conclusions: Plan complexity in oART reflects the optimization paradigm and patient-specific anatomy rather than daily adaptation. PCMs can serve as surveillance indicators flagging high-risk fractions to support SPC-based monitoring. Full article

Objective: This study examines the potential of natural language processing and text mining to automate the systematic review process in clinical psychiatry, a field that traditionally relies on domain experts and can be time-consuming, prone to human bias and errors. The study compares the classification of review articles by domain experts with that facilitated by machine algorithms. Methods: Using data from PubMed, 160 abstracts related to “transcranial magnetic stimulation” and “autism” were classified into “treatment” and “non-treatment” categories by both human reviewers and a computer algorithm. The computer algorithm, employing topic modeling in text mining, was compared to human reviewers, including two psychiatrists, a biostatistician, and a medical student. Results: The accuracy of human classifications ranged from 68% to 85%, with inter-rater reliability (Kappa statistic) between 0.40 (fair to moderate) and 0.64 (substantial). Intra-rater reliability, tested by reclassification after three months, varied from 0.38 to 0.82. Conclusions: The findings highlight the consistency and reproducibility of computational approaches compared to human classification, which exhibited both inter-rater and intra-rater variability. Differences in reviewer performance were observed; however, these patterns should be interpreted cautiously, as the study was not designed to directly assess cognitive or decision-making processes. Full article

Brucellosis, a zoonotic disease caused by Brucella, requires rapid, accurate, and sensitive diagnostic methods for effective prevention and control. This study presents the development of a fluorescence microsphere immunochromatographic assay (QDMs-ICA) for detecting anti-Brucella antibodies in bovine serum and milk. Lipopolysaccharide (LPS) from the Brucella abortus strain A19 was immobilized on the nitrocellulose membrane (NC membrane) as the test line (T-line), while rabbit anti-SPG polyclonal antibody was applied as the control line (C-line). Recombinant streptococcal protein G conjugated with quantum dot microspheres (QDMs-SPG) served as the detection conjugate. After optimizing the preparation parameters of QDMs-ICA, the method demonstrated sensitivities of approximately 0.98 IU/mL for bovine serum and 1.56 IU/mL for milk. No cross-reactions were observed with antibody-positive sera from Coxiella burnetii, Mycobacterium avium paratuberculosis, Mycobacterium tuberculosis, Chlamydia abortus, Bacillus anthracis, Escherichia coli O157:H7, Vibrio cholerae or Salmonella, indicating excellent specificity. In intra- and inter-batch repeatability tests, the coefficient of variation (CV) remained below 15%, confirming good reproducibility. The detection limit remained stable after storage at 37 °C for 7 days. Parallel testing of 150 bovine serum samples and 80 milk samples showed a high degree of concordance with the ID-VET commercial kit, with coincidence rates of 97.3% and 96.3%, respectively. These results demonstrate that QDMs-ICA offers high specificity, sensitivity, repeatability, and reliability, making it an effective tool for the rapid detection and epidemiological monitoring of brucellosis. Full article

Intra-articular soft connective tissues such as synovium and adipose tissue play a crucial role in governing joint homeostasis and disease progression in various forms of arthritis. In the knee, like many synovial joints, adipose tissue forms an integrated anatomic and functional unit with the joint-lining synovium, and the most prominent adipose depot is the infrapatellar fat pad (IFP). With growing evidence that lipid profiles in the synovium–IFP unit shift during progression of joint diseases like osteoarthritis (OA), there is strong impetus for consistent tissue collection approaches and reproducible subsequent lipid characterization. Here, we present a standardized dissection and low-input untargeted lipidomics workflow optimized for mouse knee synovium and IFP, to enable comprehensive lipid profiling. Synovium/IFP from multiple joints are pooled to increase input mass and guarantee robust lipid yield, followed by lipid extraction and high-resolution liquid chromatography-mass spectrometry (LC–MS) acquisition for global, untargeted lipidomic profiling. The analysis workflow encompasses robust feature detection, accurate lipid annotation, data transformation and normalization. These steps enhance comparability across samples, particularly those with low input amounts, while minimizing technical variance and batch effects. Using this approach, we detect a broad spectrum of lipid species spanning the major lipid categories. As expected for untargeted discovery, a subset of non-lipid species is also observed. This protocol provides a practical framework for robust, reproducible lipidomics in murine intra-articular soft tissues to support future disease-specific biomarker and drug target discovery in OA and other joint diseases. Full article

Microscopic pore structure strongly controls hydrocarbon storage and flow in carbonate reservoirs, but objective and continuous pore-type classification remains difficult because carbonate pore systems are multiscale, heterogeneous, and commonly interpreted using experience-based criteria. This study develops a reproducible workflow that integrates 912 mercury-intrusion capillary pressure (MICP) datasets from 34 wells with 474 paired thin-section and core-photograph observations from the S oilfield. Principal component analysis (PCA) reduces eight pore-structure parameters to three interpretable components that describe pore-throat scale, distribution uniformity, and connectivity/displacement behavior, retaining 87.63% of the total variance. K-means clustering identifies four pore types for dolomite and four for limestone, with k = 4 selected using the elbow criterion, silhouette coefficient, centroid interpretability, and petrographic consistency. Modified injection-to-final-state analysis (MIFA) is used as an internal MICP-based consistency check rather than as a fully independent validation; paired micro-observations provide cross-scale validation with 81.22% agreement. Lithology-constrained GR, SP, and AC response windows are then used for intra-field upscaling to uncored intervals, and field-scale back-checking shows 87% agreement with existing geological interpretations. The workflow reduces interpreter subjectivity, provides physically interpretable pore-type criteria, and is applicable to carbonate reservoirs with comparable MICP, petrographic, and logging constraints. Full article

High-resolution GPS trajectories pose a geospatial processing challenge: transforming temporally ordered observations into structured spatial representations that retain intra-trip state transitions at metropolitan scale. This study develops and validates a scalable geospatial transformation workflow for detecting and structuring recurrent mid-trip stops from large-scale trajectory data. Using approximately 97 million GPS observations from Seoul’s public bike-sharing system, stopping episodes are identified through speed-based segmentation and density-based spatial clustering (DBSCAN). Recurrent stopping hotspots are attributed with spatial context via a land-use overlay and proximity analysis to pedestrian crossings. Sequential transitions between recurrent hotspots are represented as directed and weighted hotspot-to-hotspot networks, whose structural properties are evaluated using connectivity, clustering, path length, and modularity metrics under degree-preserving randomization. The workflow emphasizes explicit parameterization and modular processing, aligning with reproducible GIS-based spatial analytical frameworks. By converting fine-grained trajectory observations into validated mesoscopic connectivity representations, the framework provides a transferable geospatial processing pipeline for extracting structured connectivity information from high-resolution trajectory datasets. Full article

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

Background and Objectives: Endoscopic ultrasound point shear-wave elastography (EUS-pSWE) bypasses subcutaneous fat and may provide weight-independent liver stiffness measurements; however, data on reproducibility and quality criteria remain limited. This study aimed to evaluate the intra-observer reproducibility and short-term variability of EUS-pSWE. Materials and Methods: In this single-center prospective cohort study (December 2024–February 2025), 120 consecutive adults undergoing diagnostic EUS were enrolled. For each hepatic lobe, 10 consecutive measurements were obtained and grouped into two sequential blocks of five measurements without scope repositioning. Intra-observer reproducibility was assessed using intraclass correlation coefficients (ICC3,1). The agreement between acquisition runs and determinants of short-term variability was also evaluated. Same-day vibration-controlled transient elastography (VCTE) served as an external comparator. Results: Forty-six participants were obese (BMI ≥ 30 kg/m²). The mean VCTE stiffness was 6.24 kPa, while the mean EUS-pSWE stiffness was 9.40 ± 5.64 kPa. Among examinations meeting IQR/Median < 30% quality criteria, reproducibility was excellent (left ICC 0.97 [0.95–0.98]; right ICC 0.92 [0.86–0.95]) and consistent across BMI strata. EUS-pSWE correlated strongly with VCTE (r = 0.81, p < 0.001). In contrast, agreement between consecutive acquisition runs was low, indicating increased short-term variability. EUS-pSWE quality pass rates based on IQR/Median criteria were modest (left 56.7%, right 41.7%, both lobes 23.3%), although all measurements fulfilled device-specific validity criteria (VSN > 60%). Age and BMI were not significant predictors of variability. Conclusions: EUS-pSWE demonstrates excellent intra-observer reproducibility under quality-controlled conditions and shows a strong correlation with VCTE. However, short-term variability between acquisition runs and limited feasibility based on conventional quality thresholds should be considered. EUS-pSWE appears to be a promising modality for liver stiffness assessment, warranting further validation of quality criteria and clinical thresholds. Full article

Background: In this study, it was aimed to compare transrectal ultrasound (TRUS)- and magnetic resonance imaging (MRI)-derived prostate-specific antigen density (PSAD) in patients with gray-zone PSA levels (4–10 ng/mL), evaluate their diagnostic performance for clinically significant prostate cancer (csPCa), and assess the clinical implications of reclassification across commonly used thresholds. Methods: We retrospectively analyzed 202 men who underwent both TRUS and multiparametric MRI between January 2020 and June 2025. Prostate volume was measured using the ellipsoid formula for TRUS and contour-based planimetry for MRI. PSA density (PSAD) was calculated as total PSA (tPSA, ng/mL) divided by prostate volume (mL) for each modality: TRUS-PSAD and MRI-PSAD. Agreement between modalities was evaluated using Bland–Altman plots and correlation analyses. Reclassification at PSAD thresholds of 0.15, 0.20, and 0.30 ng/mL/mL was assessed using Cohen’s κ and net reclassification improvement (NRI). Diagnostic performance for csPCa (ISUP grade group ≥ 2) was evaluated with ROC analysis and the DeLong test. Inter- and intra-observer reproducibility was determined using intraclass correlation coefficients (ICC) and Cohen’s κ. Clinical utility was assessed by decision curve analysis (DCA). Results: MRI-derived prostate volumes were significantly lower than TRUS-derived volumes (median 47.0 vs. 52.5 mL, p < 0.001), resulting in higher MRI-PSAD values (median 0.14 vs. 0.12 ng/mL/mL, p < 0.001). Bland–Altman analysis demonstrated a negative bias for prostate volume (−3.2 mL) and a positive bias for PSAD (+0.03). Strong correlations were observed between TRUS and MRI measurements (r = 0.96 for volume and r = 0.94 for PSAD). MRI-PSAD frequently reclassified patients into higher risk categories, yielding positive net reclassification improvement for cancer cases across all thresholds, while introducing some negative reclassification among non-cancer cases. ROC analysis showed comparable overall diagnostic performance between TRUS-PSAD and MRI-PSAD (AUC 0.681 vs. 0.679, p = 0.91). However, MRI-PSAD demonstrated higher sensitivity at predefined thresholds at the expense of reduced specificity, reflecting a threshold-dependent shift rather than improved discrimination. Reproducibility was higher for MRI-derived measurements (ICC = 0.94; κ = 0.83) compared with TRUS (ICC = 0.86; κ = 0.71). Decision curve analysis indicated that MRI-PSAD, particularly when combined with PI-RADS ≥ 3, provided the greatest net clinical benefit at lower threshold probabilities (5–15%). Conclusions: MRI-derived PSA density produces systematically higher values than TRUS-based measurements due to inherent differences in prostate volume estimation. While this results in increased sensitivity at standard thresholds, overall discrimination remains unchanged. These findings support the use of modality-specific PSAD thresholds rather than uniform cutoffs across imaging techniques. In clinical practice, MRI-PSAD may provide additional value when interpreted in conjunction with PI-RADS, primarily through improved threshold calibration rather than enhanced diagnostic accuracy. Full article

Background/Objectives: Multimodal large language models (MLLMs) are increasingly evaluated for diagnostic tasks in medical imaging, including radiographic interpretation. However, most studies focus primarily on binary fracture detection and rarely assess clinically relevant fracture characteristics such as displacement or intra-articular extension, which influence treatment decisions. In addition, most evaluations rely on single-run inference designs that do not assess response reproducibility. This study evaluated the diagnostic performance and inter-run reliability of five MLLMs for radiographic assessment of distal radius fractures. Methods: Fifty fracture-positive distal radius radiographs were evaluated by five MLLMs (ChatGPT 5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent zero-shot inference runs (n = 1250 observations). Diagnostic tasks included fracture detection, intra-articular extension, and displacement. Sex and age were exploratory endpoints. Performance was summarized using sensitivity (fracture detection) and accuracy (other tasks), with inter-run reliability assessed via Fleiss’ κ. Results: Performance varied across tasks and models. Fracture detection sensitivity ranged from 39.6% to 99.6%, with two models exceeding 90%. Intra-articular extension accuracy ranged from 51.6% to 55.6%, consistent with chance-level performance. Displacement classification ranged from 34.8% to 70.4%. One model achieved substantial inter-run agreement across binary tasks (κ > 0.60), whereas two models showed slight agreement (κ < 0.20). Conclusions: Only two models exceeded 90% sensitivity for fracture detection, while intra-articular extension remained at chance level (≤55.6%). Substantial inter-run reliability (κ > 0.60) was observed in only one model. These findings indicate that current MLLMs do not reliably support multidimensional fracture assessment and that single-run evaluations overestimate robustness. Full article

Background/Objectives: The objective assessment of natural tooth mobility remains challenging in clinical practice. This pilot study aimed to investigate the feasibility, repeatability, and agreement of a modified implant stability measurement system adapted for natural teeth using a custom-fabricated titanium bracket and a modified SmartPeg. Methods: Sixteen systemically healthy patients (10 males, six females) and 94 single-rooted permanent teeth with varying mobility grades were included. The tooth mobility was assessed using the Miller Mobility Index, Periotest M, and resonance frequency analysis (RFA) with the Osstell Beacon device. For the Osstell measurements, a custom titanium bracket bonded to the buccal tooth surface allowed for the placement of a modified SmartPeg. Each tooth was measured twice under standardized conditions, and mean values were recorded. The statistical analyses included Spearman correlation analysis, Cohen’s kappa for agreement with Miller categories, and intraclass correlation coefficients (ICCs) to assess the measurement repeatability. Results: The mean Periotest value was 12.70 ± 13.69, and the mean ISQ (implant stability quotient) value was 69.45 ± 19.37. The repeated measurements demonstrated excellent intra-examiner repeatability for both devices (ICC > 0.95). The Periotest values showed substantial agreement with the Miller mobility grades (κ = 0.763; p < 0.001), whereas the Osstell values demonstrated weak agreement with these ordinal categories (κ = 0.094; p = 0.048). A strong negative correlation was observed between the Periotest and Osstell measurements irrespective of the scales (r = −0.865; p < 0.001). Conclusions: In natural dentition, the resonance frequency analysis demonstrated reproducible measurements under controlled experimental conditions and showed measurable associations with conventional mobility assessments. However, the method remains investigational. The findings do not establish clinical validity for the routine assessment of natural tooth mobility. Further studies with larger sample sizes and statistical models accounting for patient-level clustering are required before clinical implementation can be considered. This study is registered at ClinicalTrials.gov (NCT07188168). Full article

Background: Comprehensive genomic profiling (CGP) is increasingly used in precision oncology to identify actionable genomic alterations and guide targeted therapies in solid tumors. However, the clinical implementation of CGP assays requires rigorous analytical validation to ensure accurate and reproducible detection of diverse genomic alterations across heterogeneous tumor samples. Despite rapid advancements in next-generation sequencing technologies, there remains a need for validated CGP platforms that demonstrate reliable performance and readiness for routine clinical use. Methods: This study evaluated the analytical and clinical performance of a CGP assay capable of detecting multiple genomic alteration types, including single nucleotide variants (SNVs), insertions/deletions (Indels), copy number variations (CNVs), gene fusions, and tumor mutational burden (TMB). Validation was conducted using patient-derived 117 FFPE tumor samples, external proficiency testing materials, and reference standards. Assay performance was assessed through comparison with orthogonal methods and through evaluation of reproducibility, limit of detection, and TMB concordance. Results: The assay demonstrated excellent analytical performance, achieving 100% sensitivity, specificity, and accuracy for variant detection across evaluated samples. Strong concordance was observed for TMB estimation (R² = 0.9925), with consistent classification of TMB-high cases. The assay showed robust inter- and intra-run reproducibility and reliable detection of low-frequency variants. Limit-of-detection (LOD) analysis confirmed accurate SNV detection at approximately 1% variant allele frequency and reliable RNA fusion detection at low input levels. Conclusions: The validated CGP assay provides accurate, reproducible, and comprehensive detection of clinically relevant genomic alterations in solid tumors. These results support its suitability for routine clinical deployment, enabling reliable genomic profiling to inform precision oncology treatment decisions. Full article