Search Results (2,737)

Calcaneal stress fracture is an uncommon osseous cause of heel and hindfoot pain. A 33-year-old woman with chronic underweight status (body mass index, 16.59 kg/m²) presented with a 2- to 3-week history of right lateral hindfoot pain that reportedly began during treadmill walking in association with a perceived twisting event. The medical record documented tenderness in the region of the calcaneofibular ligament and a negative talar tilt test, but heel-specific examination was incompletely documented. Magnetic resonance imaging (MRI) obtained at the initial evaluation was formally interpreted as showing a calcaneal tuberosity stress fracture and quadratus plantae strain, with no abnormality reported in the ankle ligament complexes. On retrospective MRI review, an incomplete, vertically oriented fracture line extending to the cortex without articular extension or osseous displacement was identified, with surrounding marrow signal abnormality meeting the imaging criteria for Nattiv grade 4. Weight-bearing radiographs obtained 4 days later showed a corresponding sclerotic band. This case illustrates MRI–radiographic correlation in a calcaneal tuberosity stress fracture presenting with lateral hindfoot pain and supports inclusion of osseous injury in the differential diagnosis. Full article

Background/Objectives: Deep learning has achieved remarkable success in medical image analysis; however, limited model interpretability remains a major barrier to its clinical translation. MRI-negative temporal lobe epilepsy (TLE) is characterized by the absence of readily identifiable structural abnormalities on conventional MRI. Methods: We propose a hierarchical, multiscale 3D residual network (H-MSResNet) combined with layer-wise relevance propagation (LRP). The study included structural T1-weighted MRIs from 101 patients with MRI-negative TLE and 101 healthy controls. Model classification performance was evaluated using a fivefold cross-validation approach. Subsequently, group-level LRP analysis was integrated with a standard brain atlas to quantify the anatomical regions contributing to the model’s decisions. Results: H-MSResNet achieved an average classification accuracy of 76.27% and a best single-fold accuracy of 82.50%, with higher accuracy, specificity, and F1 score but lower sensitivity and AUC than the two comparison models. Group-level, LRP-based analysis combined with a standard brain atlas revealed that the model’s decision-making primarily focused on structures related to the temporal lobe and limbic system, including regions such as the hippocampus, parahippocampal gyrus, and amygdala. Population-level attribution also showed interhemispheric differences across several regions. Conclusions: Structural MRIs of MRI-negative TLE contain latent discriminative information that can be recognized by deep learning models. The H-MSResNet and LRP framework provides viable, explainable methodological support for the computer-aided diagnosis and brain region analysis of MRI-negative epilepsy. Full article

Medical image segmentation is a critical task in computer-assisted diagnosis, but accurate delineation remains challenging in sensor-captured dermoscopic and endoscopic images because of low contrast, blurred boundaries, acquisition artifacts, and large appearance variations. Although CNN-based methods are effective in capturing local texture and boundary cues, they often struggle to explicitly model long-range dependencies and global structural relationships. Transformer-based architectures can capture global context, but their self-attention mechanism may become computationally costly when processing high-resolution feature maps. To address these challenges, we propose MGA-UNet, a frequency-aware multi-scale encoder–decoder segmentation framework that integrates wavelet-based frequency decomposition with Mamba-based long-range dependency modelling. Specifically, the Wavelet-Mamba feature extraction backbone (WMB) decomposes features into low- and high-frequency components to enhance boundary-aware representation, the Gated Multi-scale Aggregation Module (GMAM) aggregates parallel multi-scale encoder features and applies a content-dependent gate to the fused response, and the Adaptive Sparse Attention Module (ASAM) refines bottleneck representations with sparse attention for global semantic modelling. Across three independent runs with random seeds 42, 123, and 2026, MGA-UNet achieves mean Dice Similarity Coefficients of $88.92\pm 0.04\%$, $88.01\pm 0.07\%$, and $85.91\pm 0.04\%$ on ISIC2018, ISIC2017, and Kvasir-SEG, respectively. These results demonstrate competitive segmentation performance among the compared representative CNN-based, Transformer-based, and Mamba-based methods, including the recent H-VMUNet baseline. These results indicate that frequency-domain decomposition and state-space modelling can complement each other for accurate medical image segmentation, particularly in images with ambiguous boundaries and complex background interference. Full article

In response to the challenge of balancing accuracy and generalization in medical image classification using convolutional neural networks (CNNs), this paper proposes ResKAN18, a hybrid structure that embeds the learnable spline function of the Kolmogorov–Arnold network (KAN) into the ResNet18 classification head for intelligent diagnosis of COVID-19 in chest X-ray images. ResKAN18 includes three variants: ResKAN18—Large (four layers of KAN, hidden-layer dimensions [256, 128, 64]), ResKAN18—Standard (four layers of KAN, hidden-layer dimensions [128, 64, 32]), and ResKAN18—Simple (three layers of KAN, hidden-layer dimensions [64, 32]), which can achieve a flexible balance between accuracy and efficiency with different depths of KAN. A systematic comparison has been conducted between four classic CNN baselines including ResNet18, VGG16, DenseNet121, ShuffleNetV2, and three ResKAN18 variants on a benchmark dataset containing 3880 chest X-rays (COVID-19, normal, viral pneumonia). The results have shown that ResKAN18—Large can achieve an accuracy of 98.80% on the independent test set, which is 1.21% higher than ResNet18 and 0.69% higher than DenseNet121—its parameter count is 13.97M, inference delay is 8.25 ms/image, and training–validation accuracy difference is only 1.50%. The accuracy and performance stability of the dataset under random partitioning conditions are superior to the other two variants and all classic CNN baselines. All ResKAN18 variants have achieved zero missed diagnoses for COVID-19, while ResNet18 has shown missed diagnoses (0.9944). Taking into account the trade-off between accuracy, generalization, and inference efficiency, ResKAN1—Large is recommended as the default configuration, while for edge deployment scenarios with severely limited resources, ResKAN18—Simple can provide a cost-effective alternative with an extremely low latency of 2.50 ms/image and only 3.1% parameter increment compared to ResNet18. Full article

Background: Ovarian torsion is a pediatric surgical emergency in which timely diagnosis and preservation of ovarian tissue are important considerations for future reproductive and endocrine function. This study aimed to describe the clinical characteristics, diagnostic evaluation, surgical management, and outcomes of pediatric ovarian torsion and explore factors associated with ovarian preservation and oophorectomy. Methods: A retrospective, single-center cohort study was conducted including 56 girls with surgically confirmed ovarian torsion treated at a tertiary pediatric center between January 2010 and December 2025. Secondary analyses of factors associated with oophorectomy were considered exploratory. Demographic, clinical, imaging, operative, and outcome data were extracted from medical records. Variables included symptom duration, degree of torsion, laterality, diagnostic assessment, gynecologist consultation, associated pathology, intraoperative macroscopic color change following detorsion, oophoropexy, oophorectomy, postoperative complications, and recurrence. Univariable comparisons were performed using the Mann–Whitney U test, chi-square test, Fisher’s exact test, or Monte Carlo exact test, as appropriate. Exploratory multivariable logistic regression, supplemented by Firth penalized logistic regression, was performed in the postnatal cohort to assess factors associated with oophorectomy. Results: The median age at diagnosis was 10 years (range 0–17) and the median duration of symptoms before admission was 18 h (IQR 8–72). Ovarian preservation was achieved in 35 patients (62.5%), whereas complete oophorectomy or adnexectomy was performed in 21 (37.5%). Complete macroscopic color change following detorsion was observed in 19 patients (33.9%), partial color change in 13 (23.2%), and no macroscopic color change in 24 (42.9%). Prenatal ovarian torsion represented a clinically distinct subgroup and was associated with a high oophorectomy rate (8/8, 100%), compared with 13/48 (27.1%) among patients with postnatal torsion (p < 0.001). In the postnatal cohort, complete oophorectomy or adnexectomy occurred in 19.4% of patients with preoperative gynecologist consultation and 41.2% of those without consultation (unadjusted OR 0.34, 95% CI 0.09–1.28; p = 0.173). In exploratory multivariable analysis adjusting for age and calendar period, preoperative gynecologist consultation was not independently associated with oophorectomy (adjusted OR 0.41, 95% CI 0.09–1.78; p = 0.234); Firth penalized regression yielded similar results. Associated pathology was identified in 66.1% of patients. Documented recurrence occurred in three patients (5.4%), and postoperative complications were uncommon (3.6%). Conclusions: Ovarian preservation was achieved in more than half of pediatric patients with ovarian torsion, whereas prenatal torsion was associated with a high oophorectomy rate. Preoperative gynecologist consultation was associated with a numerically lower unadjusted oophorectomy rate; however, this association was not statistically significant in either unadjusted or adjusted analyses, and residual confounding and changes in clinical practice over the 15-year study period cannot be excluded. Full article

Infective endocarditis (IE) continues to represent a major challenge for global health systems. In 2019, its annual incidence was estimated at 13.8 cases per 100,000 individuals, contributing to approximately 66,300 deaths worldwide. Due to its high morbidity and mortality rates, enhancing preventive measures has become a priority in both clinical practice and ongoing research efforts. Since the publication of the 2015 ESC Guidelines for the management of IE, several pivotal studies have emerged, prompting a re-evaluation and potential update of the existing recommendations. One growing concern is the increasing antibiotic resistance among oral streptococci, particularly to macrolides such as azithromycin and clarithromycin, which now show higher resistance levels than penicillin. Changes in national antibiotic stewardship programs may have inadvertently contributed to a rise in IE incidence, in part due to altered prophylactic practices. At the same time, advances in diagnostic modalities—including more widespread and targeted use of echocardiography in patients with positive blood cultures for organisms like Enterococcus faecalis, Staphylococcus aureus, and various streptococci—have likely improved detection rates. Additionally, innovations in imaging, particularly computed tomography (CT) and nuclear medicine techniques, have enhanced the diagnosis of IE, especially among patients with prosthetic heart valves or implantable cardiac devices. This has allowed for better characterization of patient populations, aiding in the refinement of diagnostic criteria and therapeutic approaches. Furthermore, updated antibiotic treatment protocols, informed by EUCAST’s antimicrobial susceptibility data, have helped tailor antimicrobial regimens to current resistance trends. The combination of improved diagnostic sensitivity and evolving microbial resistance patterns has also led to an increased number of patients being considered for cardiac surgery as part of their treatment pathway. This review seeks to synthesize the latest findings and guideline revisions, offering an integrated overview of recent progress in the diagnosis, medical treatment, and surgical management of infective endocarditis. It will also explore current therapeutic strategies and operative indications in light of the most recent evidence. Full article

Background/Objectives: Primary thyroid lymphoma accounts for approximately 0.2–2% of thyroid malignancies. Plasmablastic lymphoma (PBL), an aggressive large B-cell neoplasm with plasma-cell differentiation and frequent loss of conventional B-cell markers, is a rare thyroid presentation. We report a thyroid PBL presenting as an acute surgical airway emergency in an immunocompetent patient and highlight the diagnostic and management pitfalls that distinguish this from anaplastic thyroid carcinoma. Case Presentation: A 75-year-old man without any significant past medical history presented with rapidly progressive right-sided neck swelling, dysphagia, inspiratory stridor, and respiratory failure. Imaging demonstrated a large, thyroid-centered mass with tracheal involvement, initially raising concern for anaplastic thyroid carcinoma. Histopathology revealed a diffuse infiltrate of large, atypical cells with immunoblastic and plasmablastic morphology. The neoplastic cells were CD20- and CD138-negative but strongly MUM1-positive, with lambda light-chain restriction, bright CD38 by flow cytometry, a Ki-67 proliferation index exceeding 95%, aberrant cytoplasmic CD3 expression, and a MYC::IGH rearrangement, supporting a diagnosis of PBL. Staging identified extranodal perinephric disease and mesenteric lymphadenopathy, consistent with disseminated extranodal Ann Arbor stage IV disease. The patient underwent systemic treatment and initially had an excellent response; however, one month after the last treatment cycle they presented to the hospital with a mass consistent with recurrence. Discussion: Rapid growth, fixation, and tracheal invasion strongly suggest anaplastic thyroid carcinoma in routine clinical practice. However, plasmablastic lymphomas can present similarly and require fundamentally different treatment. In this case, loss of conventional B-cell markers, CD138 negativity, and aberrant cytoplasmic CD3 expression created substantial diagnostic challenges. Light-chain restriction, plasma-cell-associated markers, flow cytometry, and MYC cytogenetics were vitally important. Conclusions: Thyroid-presenting PBL is exceptionally rare and may closely mimic anaplastic thyroid carcinoma, including presentation with life-threatening airway compromise and tracheal involvement. This case highlights several diagnostic pitfalls: CD138 negativity despite plasma-cell differentiation, and aberrant cytoplasmic CD3 expression. Prompt airway stabilization, adequate tissue acquisition, broad immunophenotyping, light-chain assessment, flow cytometry, EBV/HHV8/ALK testing, and MYC cytogenetics are essential for accurate diagnosis and lymphoma-directed treatment. Full article

Systolic anterior motion (SAM) of the mitral valve can occur either in association with or in the absence of hypertrophic obstructive cardiomyopathy (HOCM). SAM induces dynamic left ventricular outflow tract obstruction (LVOTO) and, in the majority of cases, is associated with a substantial degree of mitral regurgitation (MR) that significantly impacts patient morbidity and mortality. This narrative review explores the contemporary understanding of the pathophysiology, diagnosis, and management of SAM, focusing particularly on surgical strategies and the novel therapeutic class of cardiac myosin inhibitors. Extended septal myectomy remains the gold-standard treatment for HOCM-related SAM, yielding superior long-term outcomes compared to alcohol septal ablation (ASA). Advanced imaging modalities, including three-dimensional transesophageal echocardiography (3D-TEE), enable precise pre-operative characterization of the mitral valve apparatus. Some patients may benefit from septal reduction strategies while concomitant mitral valve interventions are generally reserved for highly selected cases with intrinsic valve pathology or persistent residual SAM, thereby avoiding unnecessary valvular manipulation and its potential hemodynamic risks. Mavacamten, a selective cardiac myosin inhibitor, represents an important advance in pharmacological management, achieving a mean LVOT gradient reduction of 37.2 mmHg in symptomatic patients. Furthermore, data from the MARVEL registry confirm the real-world clinical efficacy of mavacamten in obstructive hypertrophic cardiomyopathy, with 86% of patients successfully down-staged to NYHA functional class I–II. Although ASA serves as a viable alternative to surgery, it entails a higher risk of conduction abnormalities requiring permanent pacemaker implantation and subsequent re-intervention. Beyond classical hypertrophic SAM, this review addresses the diagnosis and management of post-mitral repair complications and non-hypertrophic variants. Optimal management and risk stratification remain an evolving field requiring a multidisciplinary Heart Team approach, leverage of advanced imaging, and adoption of novel medical therapies. Interventional strategies must be carefully tailored to maximize the efficacy-to-safety profile on an individualized patient basis. Full article

Background: Elevated plasma homocysteine (HCY) is a risk factor for Alzheimer’s disease (AD), but its relationship with structural brain changes across the AD continuum remains unclear. The choroid plexus (CP) regulates cerebrospinal fluid homeostasis and may interface with peripheral metabolic signals. Whether HCY relates to CP structural alterations and disease progression remains unknown. Methods: We analyzed 819 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (229 cognitively normal (CN), 397 with mild cognitive impairment (MCI), and 193 with AD dementia). Multinomial logistic regression assessed associations between HCY and diagnosis under stepwise covariate adjustment. Phenotype-wide structural magnetic resonance imaging (MRI) mapping identified HCY-associated signals. Cox models evaluated associations of CP volume (CPV) with CN-to-MCI and MCI-to-AD dementia conversion and whether CPV added prognostic discrimination beyond baseline disease-severity markers. Independent human CP single-nucleus and spatial transcriptomic datasets were reanalyzed to characterize epithelial expression states and their spatial organization in a hypothesis-generating analysis. Results: Higher HCY was associated with MCI and AD dementia; however, the AD association attenuated after adjustment for renal function, vitamin B12, and medications, whereas the MCI association remained stable. CPV was among the HCY-associated MRI signals that persisted after progressive covariate adjustment. Right and bilateral CPV showed model-dependent associations with MCI-to-AD dementia conversion. In the disease-severity sensitivity analysis, larger right and bilateral CPV remained associated with a higher risk of progression from MCI to AD dementia. Single-nucleus analysis identified two CP epithelial states with relatively high expression of one-carbon metabolism-related genes, termed one-carbon metabolism-enriched epithelial state A (OCM-Epi-A) and state B (OCM-Epi-B). Donor-level pseudobulk analysis did not identify pathway enrichment after false discovery rate correction, whereas OCM-Epi-A–like spots were located near endothelial spots more often than expected by chance in three of the four spatial samples. Conclusions: Circulating HCY was associated with larger CPV, and larger CPV showed model-dependent associations with MCI-to-AD dementia progression. Independent transcriptomic reanalysis identified one-carbon metabolism-enriched epithelial states and their spatial organization in postmortem CP tissue, providing hypothesis-generating tissue-level context for the ADNI associations. Full article

Background/Objectives: Paget’s disease of bone (PDB) and osteoporosis are chronic metabolic bone disorders characterized by disrupted bone remodeling and increased skeletal fragility; however, the underlying mechanism of PDB remains poorly understood. Artificial intelligence (AI) has emerged as a transformative tool in medical imaging, enabling automated feature extraction and improved diagnostic classification of skeletal disorders. This study aimed to investigate whether AI could distinguish subtle variations in bone morphology between PDB and osteoporotic bone. Methods: C57BL/6 mice femurs were scanned by µCT: 16 optineurin-knockout mice with a PDB phenotype (20–26 months), 25 genetically matched wild-type Aging mice (20–26 months), and 15 ovariectomized (OVX) mice with osteoporotic bone phenotype (4.5 months). Two AI algorithms were investigated: a machine learning (ML) model using 22 µCT-derived features trained with a Random Forest (RF) classifier, and a deep learning (DL) model using a 3D convolutional neural network (3D-CNN) trained on raw µCT images. Leave-one-out cross-validation was applied to evaluate model robustness. Results: Significant differences in volumetric, density, and morphological parameters of cortical and trabecular bone were observed between PDB and osteoporosis (p < 0.05). The RF algorithm achieved 90% accuracy in distinguishing PDB from both aging- and OVX-induced osteoporosis and provided feature importance rankings that improved model interpretability. The 3D-CNN achieved classification accuracies of 70% for PDB vs. OVX and 68% for PDB vs. aging, demonstrating the feasibility of an image-based DL approach. Conclusions: AI-based RF and 3D-CNN models demonstrated promising performance in differentiating PDB from osteoporosis using µCT-derived bone features. These findings suggest potential for using AI to assist with analyzing skeletal images in the diagnosis of metabolic bone disorders. Full article

Because lesions associated with digestive system diseases are often deeply seated, embedded within complex luminal milieus, and protected by substantial local delivery barriers, conventional diagnostic and therapeutic modalities remain constrained in their targeting capability, minimal invasiveness, and precision. Microrobots, leveraging micro-scale motion, active navigation, programmable controllability, and theranostic integration, open a new avenue for the precise diagnosis and treatment of digestive system diseases. Here, we review the major actuation modalities and structural designs of microrobots and discuss recent advances in their use across the gastrointestinal and hepatopancreatobiliary systems, focusing on targeted drug delivery, biospecimen harvesting, lesion detection, and interventional treatment. We further discuss the major obstacles to progress in this field, including robust operation in complex in vivo settings, real-time imaging and closed-loop feedback control, biosafety, degradability, and eventual clinical implementation. With continued advances in high-performance materials, multimodal actuation, intelligent control, and convergence with endoscopic and medical imaging technologies, microrobots are poised to accelerate the transition of digestive disease care toward precision, intelligence, and minimally invasive intervention. Full article

Lung cancer arises from mutations in lung cells, disrupting their normal growth cycle and leading to uncontrolled cell division. These rapidly dividing cells lose function and fail to form healthy lung tissue. Several factors contribute to the difficulty of diagnosing and classifying lung nodules, including the high degree of morphological heterogeneity and overlapping characteristics between benign and malignant nodules. Recently, deep learning models have been used in computed tomography (CT)-based lung nodule diagnosis and have demonstrated diagnostic efficiency comparable to that of radiologists. This study introduces LungCNET, a high-performance multi-layer deep convolutional neural network trained on chest CT images to improve lung lesion classification efficiency and accuracy significantly. The data for the Lung Cancer convolutional neural network (LungCNET) were derived from the IQ-OTH/NCCD CT scan dataset (1097 images from 110 cases), split into training (767 images), validation (109) and a held-out test partition (221) that played no role in training or model selection. This dataset encompasses three diagnostic categories: benign, malignant, and normal lung tissues. LungCNET was evaluated against fine-tuned benchmark models that are both established and widely used, spanning architectures introduced between 2014 and 2024, including VGG16, ResNet50, InceptionV3, MobileNetV2, and YOLOv11. On the held-out test partition, LungCNET reached a macro-averaged F1-score of 95.19%, with VGG16 at 94.09% and InceptionV3 at 92.28%; these three models performed comparably, and the separation between them is small relative to the resolution of a test set of this size. LungCNET was, however, the only model to exceed 90% F1-score across all three diagnostic classes simultaneously, and recorded the highest F1-score on the benign class (91.0%), the smallest and most frequently misclassified category, where two of the six models failed entirely. These results support LungCNET as a candidate tool for lung cancer diagnosis, subject to validation on larger and independently sourced datasets. Full article

Ovarian cancer remains a leading cause of gynaecological cancer mortality, and ultrasound-based deep learning systems for its diagnosis are typically built as separate post hoc processes for classification, segmentation, and interpretability, which introduces workflow inefficiencies and may produce inconsistent predictions. This work addresses that limitation. We propose UM-TOTA (Unified Multi-Task Ovarian Tumour Architecture), a Vision Transformer (ViT)-based architecture that performs eight-class tumour classification, three-class malignancy detection, tumour segmentation, and clinical concept interpretability within a single unified framework. We integrate a concept bottleneck guided by the IOTA and O-RADS clinical guidelines to enable transparent decision-making through medical concepts that clinicians can understand, and we employ combined adaptive t-vMF Dice and boundary-enhanced segmentation losses with progressive task weighting to stabilise multi-task optimisation. We evaluated the model on the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) 2D dataset under two protocols: image-level 5-fold stratified cross-validation, and the patient-disjoint partition released with the dataset. Under cross-validation, UM-TOTA achieved 80.26% ± 1.10% accuracy (97.06% one-vs-rest macro specificity) for eight-class classification, 90.88% ± 1.14% accuracy (90.41% specificity) for malignancy detection, and 77.29% ± 1.29% Dice for segmentation. Under the patient-disjoint partition, which excludes any overlap of patients between training and testing, the corresponding values were 78.46%, 89.13%, and 75.41%, a reduction of under 2.2 percentage points on every metric. The UM-TOTA reduced the computational parameter load by approximately 65.1% relative to sequential single-task pipelines. The learned concepts aligned with established malignancy criteria, identifying vascularisation, solid components, and papillary projections as key predictors. This unified approach offers an efficient and interpretable framework for clinical ovarian ultrasound workflows. Full article

Background: Cardiac arrhythmias in children and adolescents may present with nonspecific symptoms such as precordial pain, palpitations, and syncope. We evaluated the clinical characteristics and rhythm findings of symptomatic pediatric patients and examined cross-sectional associations between reported symptoms and selected rhythm diagnoses, with particular attention to the diagnostic and management yield of ambulatory Holter monitoring. Because symptom timing is not always captured during diagnostic monitoring, associations between reported symptoms and detected rhythm abnormalities must be distinguished from temporal symptom–rhythm correlation and causation. Methods: We conducted an observational study of 119 children and adolescents aged 1–18 years, who were evaluated at a tertiary pediatric hospital for symptoms potentially suggestive of cardiac rhythm abnormalities. Data included demographic characteristics, clinical symptoms, personal and family history, resting electrocardiography, ambulatory Holter monitoring, and exercise testing where clinically indicated. The primary analysis was cross-sectional. Because symptom timing during ambulatory monitoring was not recorded, the study was not designed to establish temporal symptom–rhythm correlation or causality. Descriptive analyses were performed for the final 119-participant cohort. Selected symptom–rhythm associations were evaluated using the contingency table methods with odds ratios (ORs), 95% confidence intervals (CIs), and Fisher’s exact tests where appropriate. Holm’s adjustment was applied to the prespecified family of reconstructed symptom–rhythm comparisons. Results were interpreted according to both statistical evidence and effect-size precision. Results: The cohort comprised 119 participants, including 75 females (63.0%) and 44 males (37.0%). The most frequently reported symptoms were precordial pain (105/119, 88.2%), palpitations (80/119, 67.2%), and syncope or lipothymia (39/119, 32.8%). A family history of sudden cardiac death was reported by 28 participants (23.5%). Forty-two participants (35.3%) had a normal resting ECG, while 77 (64.7%) underwent ambulatory Holter monitoring. Among those undergoing Holter monitoring, 65/77 (84.4%) had rhythm abnormalities detected exclusively by ambulatory monitoring and not identified on the resting ECG. Holter findings resulted in at least one documented management change in 38/77 participants (49.4%). Management categories were not mutually exclusive and included lifestyle or activity adjustment in 21 participants, initiation of antiarrhythmic therapy in 19, medication monitoring in 19, and targeted cardiac imaging, including cardiac magnetic resonance imaging, in 5. In this cross-sectional analysis, reported syncope/lipothymia was strongly associated with the presence of complete atrioventricular block (10/39 [25.6%] versus 1/80 [1.3%]; OR 27.24, 95% CI 3.34–222.29; Fisher’s exact p < 0.001; Holm’s adjusted p approximately 0.0004). The unadjusted association between syncope/lipothymia and the WPW-labelled diagnosis did not remain statistically significant after Holm’s adjustment. No statistically significant associations were identified between syncope/lipothymia and PSVT or VT. Conclusions: In this pediatric cohort, ambulatory Holter monitoring identified rhythm abnormalities not detected on resting ECG in a substantial proportion of monitored participants and resulted in documented changes in clinical management in approximately half of those monitored. Reported syncope/lipothymia was strongly associated with the presence of complete atrioventricular block in the reconstructed cross-sectional analysis. Because symptom timing during monitoring was not recorded, these findings do not establish that the detected rhythm abnormalities caused the reported symptoms or that symptoms predict future rhythm outcomes. Further studies using prospectively defined symptom–rhythm event recording and longitudinal follow-up are needed to evaluate temporal correlation and prognosis. Full article

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging. Full article