Search Results (1,485)

Background/Objectives: Slipped Capital Femoral Epiphysis (SCFE) is a common, serious hip disorder in children and adolescents. Two-dimensional (2D) radiography is the gold standard for diagnosis but may not fully capture the deformity’s complexity, and it is vulnerable to positioning errors. Advances in three-dimensional (3D) imaging, such as computed tomography and magnetic resonance imaging (MRI), enable more accurate assessments. This study aimed to (1) assess the inter-rater reliability of 2D radiographic and 3D MRI measurements, and (2) evaluate the correlations and agreements between these outcomes. Methods: Patients were randomly selected from a cohort of patients aged under 16 years old and diagnosed with SCFE between January 2000 and December 2024. Southwick angles and posterior epiphyseal slip angles on 2D radiographs were independently measured by two orthopaedic surgeons. Posterior epiphyseal slip angles on 3D MRI were independently measured by two orthopaedic surgeons and two paediatric radiologists. Relationships between the three outcomes were evaluated using the Pearson correlation coefficient (r). Inter-rater reliability and agreements between the three outcomes were evaluated using the intraclass correlation coefficient (ICC) and the standard error measurement (SEM). Results: A total of 35 patients (35 hips) were recruited, with a mean age of 11.8 (1.2) years old and 19/35 (54%) females. Radiographic outcomes were moderately correlated (r < 0.75, p < 0.01) with MRI posterior epiphyseal slip angles. MRI posterior epiphyseal slip angles were systematically greater (16° on average) than both radiographic outcomes, regardless of whether contralateral correction was applied. The inter-rater reliability of radiographic outcomes was excellent (ICC > 0.85, SEM > 5.0°) and almost perfect (ICC > 0.95, SEM = 2.5°) for the MRI posterior epiphyseal slip angles measured by the paediatric radiologists. Conclusions: Findings suggest that while both diagnostic methods are reliable, radiographic measurements systematically underestimate epiphyseal slip severity by approximately 16° compared to MRI. This discrepancy could impact the accuracy of disease staging, leading to potential misclassifications. This highlights the need for a more standardised approach to evaluating SCFE, especially regarding the type of imaging used for angle measurement. Full article

This study investigated the radiopacity, elemental composition, cytotoxicity, and cytokine responses of contemporary calcium silicate-based cements containing different radiopacifiers. Four cement materials (NeoMTA2, NeoPUTTY, TheraCal PT, and One-Fil PT) were evaluated. Radiopacity was measured using digital radiography with a 10-step aluminum wedge and expressed in mm Al in accordance with ISO 6876; among three calibration models compared, the quadratic provided the best fit. Elemental composition was analyzed by SEM/EDX. Cytotoxicity was assessed on human dental pulp cells using the MTT assay, and IL-6 and IL-10 levels were quantified by ELISA. One-Fil PT (6.61 mm Al) and NeoPUTTY (6.09 mm Al) showed the highest radiopacity, whereas TheraCal PT (1.61 mm Al) did not meet ISO standards. SEM/EDX revealed tantalum in NeoMTA2 and NeoPUTTY, and zirconium in One-Fil PT and TheraCal PT. NeoPUTTY and NeoMTA2 demonstrated superior cell viability, while One-Fil PT showed the lowest. TheraCal PT and One-Fil PT increased IL-6 expression, whereas NeoPUTTY and NeoMTA2 promoted higher IL-10 levels. Within the limitations of this 24-h in vitro assessment, NeoMTA2 and NeoPUTTY exhibited more favorable short-term cytocompatibility and inflammatory profiles together with adequate radiopacity. These findings require confirmation through long-term in vivo and clinical studies. Full article

Background/Objectives: Intraoral radiographs acquired using photostimulable phosphor (PSP) plates are inherently susceptible to a wide spectrum of artifacts that can compromise diagnostic reliability and lead to unnecessary repeat exposures. Although structured taxonomies describing these artifacts have been proposed, automated methods capable of detecting and localizing multiple artifact types at the pixel level remain limited, particularly under realistic multi-class conditions. In this study, we address the problem of fine-grained, multi-class PSP artifact segmentation by systematically evaluating a deep learning-based framework and establishing a realistic baseline for this inherently challenging task. Methods: A retrospective, multi-center dataset comprising 1497 intraoral PSP radiographs (bitewing and periapical) collected from three institutions was analyzed. Pixel-level annotations were generated by expert oral and maxillofacial radiologists according to a standardized taxonomy consisting of four major artifact groups and 29 artifact classes, together with a background class. A 2D nnU-Net v2 architecture was employed as a baseline segmentation model. Model development was performed using 5-fold cross-validation, and performance was evaluated on an independent test set using Dice coefficient, Intersection over Union (IoU), Precision, and Recall. Results: Across all classes, the model achieved a mean Dice score of 0.0894 ± 0.0084 in cross-validation and 0.0952 on the independent test set, reflecting the intrinsic complexity of the task. Class-wise analysis revealed substantial variability, with higher performance in larger and visually distinctive artifacts, whereas small-scale, low-contrast, and underrepresented classes exhibited markedly reduced performance. Notably, several artifact categories were absent from the training data, resulting in a zero-shot scenario that directly constrained model generalization. Furthermore, segmentation performance demonstrated a strong dependency on class frequency, measured in terms of pixel distribution, underscoring the impact of severe class imbalance. Group-based evaluation showed relatively higher performance for pre-exposure and exposure-related artifacts compared to post-exposure and scanner-related categories. Conclusions: These findings demonstrate that large-scale, multi-class pixel-level segmentation of PSP artifacts represents a fundamentally challenging problem shaped by the combined effects of class imbalance, small object size, heterogeneous artifact morphology, and incomplete training representation. While the proposed framework confirms the feasibility of automated artifact localization, its current performance suggests greater immediate value as a quality control or screening support tool rather than a fully autonomous diagnostic system. By providing a comprehensive baseline and systematic analysis, this study establishes a benchmark for future research and highlights the critical need for imbalance-aware learning strategies, hierarchical modeling, and data-centric approaches to advance this field. Full article

Dental developmental anomalies are well documented in clinical veterinary medicine but remain rarely reported in archeological dogs. This study presents a radiologically confirmed case of an unerupted left maxillary canine associated with the absence of an alveolus for the left maxillary first molar and incisors in a dog skull from early medieval Wolin. This study aimed to determine whether the observed absence of teeth resulted from congenital agenesis, developmental arrest, ante-mortem loss, or post-depositional processes. Radiographic examination revealed a fully formed but unerupted canine, while the M¹ region exhibited a smooth bony surface without reactive remodeling, periapical radiolucencies, or signs of ante-mortem tooth loss. Differential diagnosis did not support canine agenesis, ante-mortem loss, or taphonomic damage as primary explanations. The findings most strongly support a congenital or very early developmental origin of the observed alterations. The estimated age of the individual (7–10 years) and the absence of secondary pathological changes suggest that these anomalies did not significantly impair masticatory function. Owing to the single-case nature of the material, broader population-level inferences cannot be made. This case underscores the methodological importance of radiographic imaging in archeological dental research and suggests that alveolar absence should not be automatically equated with impaired survival or poor health in this individual. 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

Objectives: To evaluate the diagnostic performance of a YOLOv8x-based deep learning model for detecting approximal, occlusal and buccal caries on paediatric panoramic radiographs and to compare its performance with human observers with different levels of clinical experience. Methods: A total of 1526 panoramic radiographs obtained from children aged 5–12 years were retrospectively analysed. Approximal, occlusal, and buccal caries in primary molars were annotated and used to train a YOLOv8x object-detection model. Model performance was evaluated on an independent test set and compared with three human observers: an intern dentist (ID), a novice specialist student (NSS), and an experienced specialist student (ESS). Diagnostic performance was assessed using precision, sensitivity, F1 score, and true positive counts. Results: The YOLOv8x model demonstrated moderate performance in detecting approximal caries (F1 score: 0.576) but showed limited performance for occlusal caries (F1 score: 0.24) and failed to detect buccal caries. The AI model showed lesion-dependent performance. For approximal caries, it performed comparably to ESS observers (p > 0.05) and better than ID (p < 0.001). Performance was poor for buccal caries (p < 0.001), and intermediate for occlusal caries, with no difference from NSS or ESS (p > 0.05) but lower than ID (p < 0.001). Overall, performance was comparable to experienced observers (p > 0.05) and superior to less experienced observers (p < 0.001). Conclusions: The YOLOv8x model achieved diagnostic performance comparable to less experienced clinicians in detecting dental caries on paediatric panoramic radiographs but did not reach expert-level accuracy. These findings suggest that deep learning models may serve as supportive tools in panoramic caries assessment rather than replacements for expert interpretation. Full article

Background: Conventional radiography faces high error rates (3–10%) due to heavy clinical workloads. While AI has emerged as a supportive tool, there is an evidence gap regarding the clinical utility of integrated AI systems in detecting both skeletal and thoracic abnormalities. Objectives: This large-scale, international multicenter study aims to validate the performance of a unified radiographic AI suite across an expanded diagnostic scope while confirming its continued robustness. Methods: A retrospective performance evaluation was conducted using 21,581 adult and pediatric X-rays collected from 20 countries. The reference standard was established through independent review by two expert readers, with adjudication of a third radiologist in cases of discordance. Diagnostic metrics, including Area Under the Curve (AUC), sensitivity, and specificity, were calculated for all 18 pathologies. Subgroup analysis was performed by patients’ age, sex, and country of acquisition. Results: For the nine findings within the expanded scope, AUC values exceeded 96.1%, with sensitivity and specificity ranges from 94.5 to 98.8% and 86.6 to 96.1%, respectively. Similarly, for the nine historically validated findings, AUCs remained above 96.1%, with sensitivity and specificity localized between 94.5 and 97.8% and 84.6 and 89.4%, respectively. Consistency was maintained across subgroups. Conclusions: The results confirm the potential of deep learning to transition from narrow, task-specific tools to a unified, high-performance diagnostic system. Full article

Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article

Background and Clinical Significant: Patent ductus arteriosus (PDA) is a common cardiovascular disorder in extremely low-birth-weight (ELBW) infants, for which surgical ligation is indicated when pharmacologic closure fails. Sudden increases in afterload combined with immature myocardial contractility can lead to post-ligation cardiac syndrome (PLCS), which usually occurs within hours after surgery. However, acute intraoperative hemodynamic collapse during PDA ligation has rarely been described. Case Presentation: A preterm infant born at 24 weeks and 3 days of gestation with a birth weight of 890 g underwent emergency PDA ligation for a hemodynamically significant PDA (hs-PDA) refractory to pharmacological treatment. Fifteen minutes after skin incision, the infant developed desaturation, bradycardia, and non-measurable noninvasive blood pressure, which required immediate hemodynamic resuscitation with manual ventilation, fluid administration, and dopamine and dobutamine infusions. Hemodynamics gradually recovered after completion of ductal ligation, whereas oxygen saturation did not fully recover. Postoperative chest radiography revealed a left-sided pneumothorax, and oxygen saturation stabilized after pleural air aspiration. The subsequent clinical course was uneventful, and typical PLCS did not develop. Conclusions: This case suggests that intraoperative hemodynamic collapse during PDA ligation may share pathophysiologic features with PLCS, and that concomitant pneumothorax can further aggravate hemodynamic instability by worsening hypoxemia and reducing venous return. Full article

Background/Objectives: Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a major contributor to morbidity and mortality in RA, yet early recognition remains challenging in routine care. The study aimed to identify clinical factors associated with hrCT-confirmed RA-ILD using a CT-verified case–control design. Methods: A single-center retrospective case–control study was designed to include RA patients who underwent chest hrCT in routine care. Cases were patients with ILD on index hrCT (n = 79) and controls were RA patients with hrCT negative for ILD (n = 59). Data were manually abstracted from clinical interview, laboratory testing, RA activity and structural assessment, respiratory examination, pulmonary function tests (PFT), chest radiography, and hrCT. Predictors were extracted from the 12 months preceding the index scan. Univariate comparisons used nonparametric tests or χ², as appropriate. Prespecified multivariable logistic regression estimated adjusted odds ratios (aORs). Sensitivity analyses included restriction to patients with available pre-index PFT, addition of respiratory examination variables, and a matched conditional logistic regression analysis. Results: In the primary multivariable model, male sex was independently associated with RA-ILD (aOR 5.31, 95% CI 1.91–14.75), and COPD/asthma was also associated (aOR 2.82, 1.05–7.56). Adding dyspnea and Velcro crackles improved discrimination (AUC 0.797 to 0.850); Velcro crackles were independently associated with RA-ILD (aOR 5.11, 1.32–19.73). Findings were directionally similar in sensitivity analyses, though precision decreased in matched models. Conclusions: In this CT-imaged real-world RA cohort, male sex, COPD/asthma, and Velcro crackles were associated with hrCT-confirmed RA-ILD; these findings should be interpreted as preliminary, as they apply to patients selected for imaging and should not be extrapolated to unselected RA populations without validation in larger, multi-center and/or prospective cohorts with systematic ascertainment. Full article

Background/Objectives: This study aimed to develop and evaluate deep learning models for the detection, localization, and classification of impacted maxillary canines, and to compare their performance with cone-beam computed tomography (CBCT) as the reference standard. Methods: This cross-sectional diagnostic accuracy study was conducted at King Abdulaziz University Dental Hospital to develop and validate artificial intelligence (AI) models for detecting and localizing maxillary canine impactions using panoramic and cone-beam computed tomography (CBCT) imaging data. A total of 641 panoramic ra and 158 CBCT scans were collected, of which 158 cases had matched panoramic–CBCT pairs for localization analysis. Images were annotated and validated by expert radiologists and orthodontists, with consensus review ensuring labeling reliability. Data augmentation expanded each panoramic and CBCT category to 500 samples for panoramic and 1000 samples for CBCT, resulting in 1935 panoramic and 5703 CBCT images after preprocessing and normalization. The datasets were divided into (training + validation) (80%) and testing (20%) subsets. MobileNetV2 architectures were used for classification, and whdiographsile, a ResNet-50–based Few-Shot Learning framework, enabled spatial localization of impacted canines. Models were trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Cohen’s kappa and 95% confidence intervals were used to assess agreement between AI predictions and expert annotations. Results: Panoramic classification achieved 94% accuracy, demonstrating the highest performance in normal cases and reduced recall for bilateral impactions. The CBCT classifier achieved 98% accuracy across positional categories. Cross-modality prediction reached 93.5% accuracy, with strong agreement compared to CBCT (Cohen’s kappa = 0.91). Expert review confirmed reliable localization of impacted canines on both imaging modalities. Conclusions: Artificial intelligence applied to panoramic radiographs supports the detection, localization, and characterization of impacted maxillary canines with performance comparable to CBCT. This approach may enable lower-radiation decision support for clinical triage. Full article

Background: Pediatric respiratory infections represent a leading cause of emergency department (ED) visits and hospitalizations. Chest X-rays are frequently used in their diagnostic evaluation, despite guideline recommendations advocating restrictive imaging strategies, particularly in young children with uncomplicated disease. Excessive imaging raises concerns regarding cumulative radiation exposure and inefficient resource utilization. Objectives: To quantify potentially unnecessary chest radiography use in hospitalized pediatric patients with respiratory infections and to identify age-related and diagnostic patterns suitable for targeted imaging optimization interventions. Methods: We conducted a retrospective observational study analyzing pediatric patients presented to the ED of a tertiary county hospital in Romania over a period of 12 months. Data regarding respiratory diagnoses, hospitalization status, patient age, and chest radiography utilization were extracted from electronic medical records. Results: Among more than 26,000 pediatric emergency presentations, 4139 children required hospitalization, of whom 1212 were diagnosed with respiratory infections. A total of 3414 chest radiographs were performed, with the highest imaging burden observed in children aged 0–4 years. Repeated imaging was common in interstitial pneumonia, bronchiolitis, and bronchial hyperreactivity. A strong negative correlation was identified between patient age and imaging frequency (r = −0.70, p < 0.001). Conclusions: Thoracic radiographs are disproportionately used in young children with respiratory infections, particularly in conditions with limited imaging indications. These findings provide an essential baseline for the development of targeted quality improvement interventions aimed at reducing unnecessary pediatric imaging. Full article

Background and Objectives: Tooth impaction is a common developmental dental anomaly characterized by the failure of eruption within the expected physiological timeframe. Permanent canines represent the second most frequently impacted teeth after third molars and may lead to functional, esthetic, and orthodontic complications. This systematic review aimed to synthesize current evidence regarding the incidence, anatomical distribution, etiological determinants, and diagnostic evaluation of permanent canine impaction. Materials and Methods: A systematic literature search was conducted in PubMed, PubMed Central, and ScienceDirect for studies published between December 2009 and December 2025. Studies reporting prevalence data, anatomical positioning, etiological factors, or imaging characteristics of permanent canine impaction were included. Study selection followed PRISMA 2020 guidelines, and 31 studies were included in the qualitative synthesis. Two independent reviewers screened titles, abstracts, and full texts. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Results: Thirty-one studies met the inclusion criteria and were included in the qualitative synthesis. The reported prevalence of maxillary canine impaction ranged from 0.97% to 7.10%, while mandibular impaction occurred less frequently. Palatal displacement represented the most common positional pattern. Major etiological factors included retained deciduous canines, dental arch constriction, supernumerary teeth, odontomas, and genetic anomalies such as lateral incisor agenesis. Cone-Beam Computed Tomography (CBCT) demonstrated superior diagnostic accuracy compared with panoramic radiography. Conclusions: Permanent canine impaction is a multifactorial condition predominantly influenced by local anatomical and environmental factors, with genetic predisposition acting as a secondary contributor. Early diagnosis and appropriate imaging assessment are essential to prevent complications such as root resorption and to optimize treatment outcomes. Full article

COVID-19 still poses a global public health challenge, exerting pressure on radiology services. Chest X-ray (CXR) imaging is widely used for respiratory assessment due to its accessibility and cost-effectiveness. However, its interpretation is often challenging because of subtle radiographic features and inter-observer variability. Although recent deep learning (DL) approaches have shown strong performance in automated CXR classification, their black-box nature limits interpretability. This study proposes an explainable deep learning framework for COVID-19 detection from chest X-ray images. The framework incorporates anatomically guided preprocessing, including lung-region isolation, contrast-limited adaptive histogram equalization (CLAHE), bone suppression, and feature enhancement. A novel four-channel input representation was constructed by combining lung-isolated soft-tissue images with frequency-domain opacity maps, vessel enhancement maps, and texture-based features. Classification was performed using a modified Xception-based convolutional neural network, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to provide visual explanations and enhance interpretability. The framework was evaluated on the publicly available COVID-19 Radiography Database, achieving an accuracy of 95.3%, an AUC of 0.983, and a Matthews Correlation Coefficient of approximately 0.83. Threshold optimisation improved sensitivity, reducing missed COVID-19 cases while maintaining high overall performance. Explainability analysis showed that model attention was primarily focused on clinically relevant lung regions. Full article

Hospitals play a central role in promoting health and well-being, yet they are also among the most resource-intensive institutions, contributing significantly to environmental degradation through high energy and water consumption, extensive waste generation, and reliance on single-use materials. This conceptual paper explores how principles of the circular economy and green economy can be integrated into hospital operations through a strategic Corporate Social Responsibility (CSR) framework, reframing sustainability as a strategic management issue rather than a compliance-driven activity. Drawing on environmental economics, sustainability studies, and institutional theory, the paper develops an integrated conceptual model structured around the environmental, social, and economic pillars of sustainability. Within this framework, four interconnected operational domains are identified: waste management and circular practices, energy consumption and renewable integration, sustainable procurement and circular supply chains, and economic and policy incentives. The social dimension explicitly encompasses healthcare staff and patients, addressing issues of workforce well-being, health education, safety, quality of life, and equitable care delivery. This advances theory by positioning strategic CSR as a function of circular and green economy, yielding a new model for hospitals, S-CSR = f(CE, GE). The paper also examines institutional and cultural barriers that constrain sustainability implementation and highlights the role of strategic leadership, governance, and system-wide innovation in overcoming these challenges. While not empirical, the study provides a theoretical foundation to inform future research, policy development, and strategic decision-making aimed at advancing sustainable, low-carbon, and resilient healthcare systems. Full article