Search Results (254)

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Prostate cancer (PCa) remains a major global health concern, with a subset of patients progressing to aggressive disease despite advances in diagnosis and treatment. Epithelial–mesenchymal transition (EMT) plays a pivotal role in tumor invasion, metastasis, and immune evasion; however, its cellular heterogeneity and clinical relevance in PCa remain incompletely understood. We analyzed single-cell transcriptomic data to characterize EMT dynamics in malignant epithelial cells. Malignant cells were identified based on aberrant copy number variation patterns, and EMT activity was quantified using AUCell. Gene expression profiling and gene set enrichment analysis identified key EMT-associated genes. By integrating bulk transcriptomic data with LASSO regression analysis, we identified five pivotal genes and constructed an EMT infiltration scoring model. The model demonstrated robust predictive performance in an external Gene Expression Omnibus validation cohort and effectively predicted early biochemical recurrence. Further analyses revealed significant associations between EMT scores, clinicopathological features, immune cell infiltration, genomic instability, and tumor immune dysfunction and exclusion scores. Pathway enrichment analysis highlighted distinct molecular characteristics between high- and low-score groups. Additionally, molecular docking using AutoDock identified potential targeted therapeutic agents for key EMT genes. Overall, this study systematically delineates EMT heterogeneity at the single-cell level and establishes a robust EMT infiltration model for prognostic prediction and therapeutic guidance in PCa, providing novel insights for precision risk stratification and individualized treatment strategies. Full article

Myocardial fibrosis is a common pathological feature of multiple cardiovascular diseases, including heart failure, hypertension, and myocardial infarction, and is associated with poor prognosis. Despite extensive research, clinically validated molecular biomarkers for early diagnosis and reliable therapeutic targets for myocardial fibrosis remain limited. Post-translational modifications (PTMs), including phosphorylation, acetylation, ubiquitination, SUMOylation, and glycosylation, are critical regulators of fibrosis-related signaling pathways, yet a systematic bioinformatics-driven identification of PTM-related hub genes has not been performed. Three publicly available GEO datasets (GSE57345, GSE133054, GSE76314) comprising cardiac tissue from heart failure and control patients were integrated. Differentially expressed genes (DEGs) were identified using the limma package, then intersected with a curated PTM gene set derived from PhosphoSitePlus and UniProt databases. Weighted gene co-expression network analysis (WGCNA) identified fibrosis-associated modules, and protein–protein interaction (PPI) network analysis via STRING and CytoHubba pinpointed hub genes. Diagnostic performance was assessed by receiver operating characteristic (ROC) analysis across independent validation cohorts. Immune cell infiltration was estimated using CIBERSORT.Molecular docking with AutoDock Vina (version 1.2.3) was performed to evaluate binding affinity of FDA-approved cardiovascular drugs against identified hub protein targets. A total of 863 DEGs were identified in the training cohort (|log₂FC| > 1.0, adjusted p < 0.05), of which 138 overlapped with the PTM gene set. WGCNA revealed a turquoise module (r = 0.79, p < 0.001) most significantly correlated with fibrosis severity. PPI analysis identified five hub genes: SIRT3, SMAD3, NEDD4L, UBC9, and CAMK2D. ROC analysis demonstrated strong diagnostic performance (AUC range: 0.82–0.92) validated in independent cohorts. Hub genes showed significant correlations with M2 macrophage infiltration. Molecular docking identified spironolactone and finerenone as top-ranked ligands with binding energies of −8.7 and −8.4 kcal/mol against SMAD3 and SIRT3, respectively. This study, which is entirely in silico and based on publicly available transcriptomic datasets, systematically identifies five PTM-related hub genes as candidate diagnostic biomarkers and prioritised drug-repurposing targets in myocardial fibrosis. These findings are hypothesis-generating and require experimental validation (protein-level confirmation, cell- and animal-based functional assays, and biophysical binding studies) before any diagnostic or therapeutic claim can be made. Full article

Background/Objectives: Disorganization of retinal inner layers (DRIL) is an important and supportive biomarker in optical coherence tomography (OCT) imaging for diagnosing the extent of diabetic macular edema (DME) in patients and anticipating visual outcomes. But the manual DRIL identification is subject to interobserver bias and requires a lot of time and effort from the experts. This research presents a novel, computerized, and clinically guided approach for the classification of DRIL that leverages the central 1 mm foveal region extracted through the annotations provided by the expert ophthalmologists and investigates the effectiveness of a transformer and Masked Auto Encoder (MAE) based foundation model (RETFound) as the primary approach. Methods: We fine-tuned and validated the RETFound model, utilizing accurate foveal center coordinates provided by the experienced ophthalmologists. Our approach emphasizes the macular region that is significant diagnostically, where DME biomarkers manifest more predominantly. To guarantee robust evaluation, the dataset was divided into 85% training and 15% held-out test sets. We performed 5-fold cross-validation exclusively on the training dataset with baseline, conservative, and moderate fine-tuning strategies, and the final model was evaluated on the independent, unseen test set. Convolutional neural network (CNN)-based transfer learning (TL) models (MobileNetV2, EfficientNetB0, InceptionV3, DenseNet121, and DenseNet169) were also assessed for comparative evaluation. Results: The RETFound model yielded the best outcomes under the conservative fine-tuning strategy, achieving a mean test accuracy (AC) of 0.9339 ± 0.0036 and an area under the curve (AUC) of 0.9660 ± 0.0028 on the independent held-out test set across the five fold-trained models. The moderate and baseline evaluations achieved comparatively lower outcomes, highlighting the effectiveness of the conservative approach. The RETFound model consistently outperformed CNN models, exhibiting stability and superior generalization for DRIL classification. We performed statistical validation using the Wilcoxon signed-rank test and 95% confidence intervals to confirm the robustness of the proposed method, and an ablation analysis showed that the fovea-centered region of interest (ROI) guidance consistently improved results when compared with whole OCT analysis. Conclusions: This research demonstrates that the deep-learning (DL) methods assisted by expert clinical knowledge with an anatomically aligned ROI could provide remarkable results in DRIL detection applications. This work attempts to establish an anatomically relevant framework for computerized DRIL identification that focuses on the highly crucial macular region, possibly helping in faster intervention and improved diagnosis in the management of DME. Full article

With the advancement of 3D digital dentistry, accurate 3D tooth segmentation has become increasingly important in orthodontics and computer-aided diagnosis. However, existing supervised approaches heavily rely on exhaustive face-wise annotations and often exhibit limited generalization across complex clinical meshes. Although self-supervised learning offers a promising alternative to alleviate annotation costs, current paradigms remain challenged by sensitivity to data augmentations, suboptimal representation learning in pure masking schemes, and the complex structural characteristics of dental geometry. To address these limitations, we propose Dental-CMAE, a graph-enhanced hierarchical Contrastive masked AutoEncoder framework tailored for 3D tooth segmentation. The framework incorporates a dual-branch masking strategy that leverages graph-based structural priors to generate distinct corrupted views while preserving intrinsic mesh topology, thereby facilitating robust reconstruction. This is integrated with a feature-level contrastive objective designed to enforce semantic consistency between co-masked regions, which enhances representation discriminability without the requirement for negative sample queues. Additionally, the architecture utilizes a hierarchical multi-scale attention mechanism that partitions feature channels into parallel streams, enabling the simultaneous capture of fine-grained morphological variations and the overarching global dental arch context. Extensive experiments demonstrate that our Dental-CMAE consistently outperforms state-of-the-art fully supervised and self-supervised methods across multiple evaluation metrics. Specifically, our framework achieves an Overall Accuracy (OA) of 95.57%, a mean Intersection-over-Union (mIoU) of 88.14%, and a mean Accuracy (mAcc) of 90.85%. Supported by these quantitative findings, our method validates its effectiveness for robust 3D tooth segmentation, highlighting its strong potential to alleviate annotation bottlenecks and improve the reliability of automated 3D digital dental workflows. Full article

Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent years, deep learning models have achieved high classification accuracy. However, their sensitivity to adversarial perturbations has become an important reliability concern in medical applications. This study suggests a robust brain tumor classification framework that combines non-negative matrix factorization (NNMF or NMF), lightweight convolutional neural networks (CNNs), and diffusion-based feature purification. Initially, MRI images are preprocessed and converted into a non-negative data matrix, from which compact and interpretable NNMF feature representations are extracted. Statistical metrics, including AUC, Cohen’s d, and p-values, are used to rank and choose the most discriminative components. Then, a lightweight CNN classifier is trained directly on the selected feature groups. To improve adversarial robustness, a diffusion-based feature-space purification module is introduced. A forward noise method followed by a learned denoiser network is used before classification. System performance is estimated using both clean accuracy and robust accuracy under powerful adversarial attacks created by AutoAttack. The experimental results show that the proposed framework achieves competitive classification performance while significantly enhancing robustness against adversarial perturbations. The findings presuppose that combining interpretable NNMF-based representations with a lightweight deep approach and diffusion-based defense technique supplies an effective and reliable solution for medical image classification under adversarial conditions. Full article

Accurate shape control is paramount for ensuring the quality of hot-rolled strip products, which is significantly challenged by the high dimensionality, inherent nonlinearity, and strong coupling of process parameters. While machine learning (ML) methods have demonstrated superior predictive performance in product quality modeling, the inherent “black-box” nature and lack of transparency severely undermine system reliability and hinder practical deployment. Existing explainable artificial intelligence (XAI) approaches predominantly rely on statistical correlations while overlooking the underlying causal mechanisms among coupled variables, which severely limits the validity of explanations. To address these limitations, a causal XAI diagnosis and optimization framework for hot-rolled strip shape is proposed. Initially, a hybrid causal structure learning module is established, which integrates domain knowledge with the NOTEARS-MLP algorithm to accurately reconstruct the causal topology and decode the complex coupling mechanisms among process parameters. Subsequently, a high-performance quality prediction module utilizing AutoML techniques is constructed to establish a robust predictive baseline. Furthermore, a causal XAI and quality optimization module is introduced, which incorporates causal constraints into standard Shapley additive explanation (SHAP) analysis for transparent diagnosis, and employs piecewise linear analysis (PLR) to generate sample-specific optimization strategies. Comprehensive experimental validation demonstrates that the prediction module significantly outperforms state-of-the-art ML approaches across multiple performance metrics. Additionally, comparative analysis reveals that the optimization strategy based on causal feature attribution exhibits 14.7% defect rate reduction over the associational baseline, which is effective, efficient and establishes a new benchmark for causal explainability in industrial process optimization applications. Full article

Background: Obstructive sleep apnea (OSA) is a complex and diverse disorder affecting almost one billion individuals worldwide. Severity of untreated OSA, measured by the apnea–hypopnea index (AHI), is noted to be associated with an increased all-cause and cardiovascular mortality. Although widely used, AHI insufficiently captures disease variability as there is a poor correlation of symptoms with the AHI. There lies individual susceptibility to the effects of OSA and that parameter alone poorly predicts cardiovascular outcomes without considering intermittent hypoxia and the hemodynamic effects of OSA. Recognition of clinical, polysomnographic, and neurophysiological phenotypes offers an opportunity to refine diagnosis, prognosis, and management strategies. Methods: We conducted a narrative synthesis of the literature involving 70 articles, focusing on quantitative and qualitative (Q2) clinical traits, polysomnographic parameters, and mechanistic insights that enable subclassification of OSA beyond AHI. Evidence from large cohorts, animal models, and pathophysiological studies were reviewed. Results: Phenotyping based on a Q2 analysis of polysomnographic respiratory event predominance, event duration, positional and REM dependence, hypoxic burden, and arousal characteristics reveals significant heterogeneity in risk profiles and therapeutic response. Apnea-predominant OSA correlates with a higher oxygen desaturation index and Epworth sleepiness scale. Hypopnea-predominant OSA correlates with a cardiometabolic disease burden and may show a more favorable response to surgical therapies. The duration of respiratory events is related to cardiovascular risk, and REM-predominant OSA independently predicts hypertension and adverse cardiovascular outcomes. Supine-predominant OSA demonstrates treatment responsiveness to auto-positive airway pressure and positional therapy. Respiratory effort–related arousals (RERAs), RERA-predominant OSA and the broader respiratory disturbance index (RDI) provide neurophysiological insight often missed by AHI-based classifications. Hypoxic burden, rather than AHI, emerged as a superior predictor of cardiovascular events and mortality. Finally, arousal frequency and periodic limb movements independently predict cardiovascular morbidity. Conclusions: Employing Q2-based phenotyping that incorporates clinical, polysomnographic, and neurophysiological markers improves risk stratification, prognosis, and individualized management of OSA. Future investigations should prioritize integrating phenotypic subclassification into diagnostic criteria and treatment planning to advance precision medicine in sleep apnea care. Full article

This prospective, single-center, case-control study investigated circulating autoantibodies (AAbs) targeting G protein-coupled receptors (GPCRs) in Long COVID (LC) patients to identify potential diagnostic biomarkers and therapeutic targets. Fifteen participants were enrolled at the LC clinic in Rome: eleven with severe LC—defined as >4 persistent symptoms (fatigue, cognitive impairment, poor exercise tolerance, dyspnea, arthralgia, or dysautonomic manifestations) >3 months post-infection—and four asymptomatic post-COVID (APC) individuals. Fatigue was assessed using the Fatigue Assessment Scale (FAS ≥ 22; severe ≥ 35). Auto-Abs against AT1R, endothelin receptor A, adrenergic (α1, α2, β1, β2), and muscarinic (M1–M5) receptors were quantified, along with blood cortisol and ACTH levels. SARS-CoV-2-specific T-cell responses to Spike and Nucleocapsid proteins were evaluated by ELISpot assay. In our small cohort, LC patients were younger, had fewer comorbidities (p = 0.03), fewer vaccine doses (p = 0.03), and higher FAS scores (33 vs. 12; p = 0.001). Mean GPCR AAbs levels were higher in LC than in APC (8.88 vs. 5.45 Units/mL; p = 0.17), indicating a coherent autoimmune signature in LC that correlates with symptom development. Morning cortisol was lower in LC (12.7 vs. 17 mg/dL; p = 0.01), and T-cell responses tended to be weaker. These findings suggest GPCR AAbs may serve as biomarkers and therapeutic targets for a subset of patients, guiding diagnosis and treatments with IV immunoglobulin or immunoadsorption. Full article

Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels. Full article

Precise medical segmentation of oral ulcers is mandatory and crucial for early diagnosis, but it remains a very challenging task due to rich backgrounds, overexposed or underexposed lesions, and the complex surrounding areas. Therefore, in order to address this challenge, this paper introduces HF-EdgeFormer, a novel hybrid model for oral ulcer segmentation on the AutoOral dataset. This U-shaped transformer-like architecture is, based on publicly available models, the second documented solution for oral ulcer segmentation and it explicitly integrates high-order frequency interactions by using multi-dimensional edge cues. At the encoding stage, a HFConv (High-order Focus Convolution) module divides the feature channels into local streams and global streams, performing learnable filtering via FFT and depth-wise convolutions. After that, it fuses them through stacks of focal transformers and attention gates. In addition to the HFConv block, there are two edge-aware units: the EdgeAware Localization module (that uses eight-direction Sobel filters) and a new Precision EdgeEnhance module (channel-wise Sobel fusion), both used in order to reinforce the boundaries. Skip connections imply Multi-dilated Attention Gates, accompanied by a Spacial-Channel Attention Bridge to accentuate lesion-consistent activations. Moreover, the novel architecture employs an innovative lightweight vision transformer-based bottleneck. It consists of four SegFormerBlock modules localized at the network’s deepest point, so we can achieve global relational modeling exactly where the largest receptive field is present. The model is trained on the AutoOral dataset (introduced by the same team that developed the HF-Unet arhitecture), but due to the limited available images, it needed to be extended by using extensive geometric and photometric augmentations (like RandomAffine, flips, and rotations). This novel architecture achieves a test Dice score of almost 82% and a little over 85% sensitivity while maintaining high precision and specificity, highly valuable in medical segmentation. These results surpass prior HF-UNet baselines while maintaining the model light, with minimal inference memory gains. Full article

Background/Objectives: Evidence-based recommendations are vital in healthcare to standardize care, reduce variability, and improve patient outcomes. In children, anaphylaxis, allergy to antibiotics, and hymenoptera venom allergy are among the commonest reasons for allergological evaluation. This work was intended to optimize the prescriptions for allergological evaluation and for the related diagnostic tests with the aim of improving the management of children with allergic diseases and promoting resource efficiency. Methods: A systematic literature review of the literature was performed to formulate recommendations on the diagnostic management of children with anaphylaxis, drug allergy, and hymenoptera venom allergy. Results: Effective management of anaphylaxis involves rapid assessment and specialist follow-up to identify triggers, prevent recurrence, and ensure patients and caregivers are educated and equipped with an adrenaline auto-injector. Integrating skin testing, specific serological assays, and oral provocation tests into the diagnostic process for children with suspected beta-lactam allergy enhances diagnostic accuracy and minimizes unnecessary avoidance of first-line antibiotics. Children and adolescents with systemic reactions to hymenopteran stings should be referred to an allergy specialist for diagnosis, risk assessment, management education, and adrenaline prescription. Conclusions: These recommendations may enhance care quality, minimize inappropriate prescriptions, and support standardized methods of diagnosis of allergological diseases in children. Full article

Background and Objectives: Cognitive deficits are common in obstructive sleep apnea (OSA), and both intermittent hypoxemia and cardiovascular comorbidity may contribute to poorer outcomes. Arterial hypertension (HTN) has been suggested as a potential modifier of cognitive function in OSA, but findings remain inconsistent. This study examined whether HTN influences baseline cognition or cognitive improvement after continuous positive airway pressure (CPAP) therapy in moderate-to-severe OSA and identified predictors of poorer post-treatment cognitive status. Materials and Methods: This prospective study involved 71 adults with newly diagnosed moderate-to-severe OSA (AHI ≥ 15). Participants underwent baseline polysomnography, Montreal Cognitive Assessment (MoCA) testing, and P300 assessments. Cognitive impairment was defined as MoCA < 26 and HTN by antihypertensive therapy, documented diagnosis, or repeatedly elevated blood pressure. All participants initiated auto-adjusting CPAP and were reassessed after three months for adherence, residual respiratory indices, MoCA, and P300 parameters. Multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of poorer cognitive outcomes. Results: CPAP therapy significantly improved apnea severity, daytime sleepiness, global cognition, and P300 latency, while P300 amplitude did not change significantly. After three months, hypertensive and normotensive patients showed similar MoCA scores, respiratory outcomes, and P300 amplitude; P300 latency remained marginally longer in hypertensive individuals. Across multivariate models, lower mean nocturnal oxygen saturation and reduced CPAP adherence independently predicted poorer cognitive outcome at follow-up. CPAP adherence demonstrated greater discriminative ability than mean nocturnal oxygenation. Conclusions: In adequately treated moderate-to-severe OSA, HTN did not significantly affect baseline cognition or short-term cognitive recovery with CPAP. Although P300 latency remained slightly prolonged in hypertensive individuals, this difference was marginal and not accompanied by cognitive deficits. Nocturnal oxygenation and CPAP adherence emerged as the strongest predictors of post-treatment cognitive status, underscoring the importance of sustained and effective therapy. These findings suggest that effective CPAP adherence and improved nocturnal oxygenation are crucial for cognitive recovery in OSA patients, regardless of hypertensive status. Full article

Background: Lung cancer is the leading cause of cancer-related mortality globally. Early detection via low-dose computed tomography (LDCT) can reduce mortality, but its implementation is challenged by the absence of objective diagnostic criteria and the necessity for extensive manual interpretation. Public datasets like the Lung Image Database Consortium often lack pathology-confirmed diagnoses, which can lead to inaccuracies in ground truth labels. Variability in voxel sizes across these datasets also complicates feature extraction, undermining model reliability. Many existing methods for integrating nodule boundary annotations use deep learning models such as generative adversarial networks, which often lack interpretability. Methods: This study assesses the effect of voxel normalization on pulmonary nodule classification and introduces a Fast Fourier Transform-based contour fusion method as a more interpretable alternative. Utilizing pathology-confirmed LDCT data from 415 patients across two medical centers, both machine learning and deep learning models were developed using voxel-normalized images and attention mechanisms, including transformers. Results: The results demonstrated that voxel normalization significantly improved the overlap of features between datasets from two different centers by 64%, resulting in enhanced selection stability. In the ROI-based radiomics analysis, the top-performing machine-learning model achieved an accuracy of 92.6%, whereas the patch-based deep-learning models reached 98.5%. Notably, the FFT-based method provided a clinically interpretable integration of expert annotations, effectively addressing a major limitation of generative adversarial networks. Conclusions: Voxel normalization enhances reliability in pulmonary nodule classification while the FFT-based method offers a viable path toward interpretability in deep learning applications. Future research should explore its implications further in multi-center contexts. Full article

Background: Urothelial bladder carcinoma (UBC) is a disease that lacks robust non-invasive laboratory biomarkers. Recently, urine liquid biopsy has emerged as a promising tool for diagnosis and surveillance of patients with these tumors. The aim of this study was to evaluate the diagnostic potential of a urinary tumor DNA detection panel, which included eight common point mutations in TERT, GPR126, FGFR3, and PIK3CA genes, in UBC. Methods: The study included patients with histologically confirmed UBC (n = 88) and patients with cystitis, bladder leiomyomas, or other non-malignant conditions (control group; n = 72). DNA was extracted from whole urine specimens. ddPCR analysis was performed using the Bio-Rad QX200 AutoDG ddPCR system. Results: Urinary tumor DNA detection panel demonstrated a sensitivity of 78.4% and a specificity of 100% (AUC−ROC = 0.892). Detection rates for the analyzed mutations were the following: 54.5%, 37.5%, 28.4%, and 38.6% for TERT, GPR126, FGFR3, and PIK3CA, respectively. Pairwise comparisons of mutant allele fractions (MAFs) for samples simultaneously positive for ≥2 mutations revealed an absence of significant differences (p > 0.05), except for the pair of FGFR3 vs. PIK3CA (p = 0.03). MAFs were not associated with any clinical and demographic features (p > 0.05), with the only exception being the tumor size: patients with tumors larger than 2.16 cm³ had higher MAFs than the rest (23.4 [1.8; 46.3] vs. 1.6 [0; 24.6] %, respectively, p = 0.02). Conclusions: Upon further validation, the presented tumor DNA detection panel for ddPCR might become a useful tool for diagnostic purposes in UBC. Full article