Search Results (10,678)

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with low-resource settings, including Ethiopia facing challenges due to limited early diagnostic services. AI-powered electrocardiography (ECG) interpretation has the potential to improve diagnostic accuracy, decentralize care, and support timely clinical decisions, but evidence on healthcare providers’ perspectives and adoption determinants is limited. This exploratory descriptive qualitative study employed 31 in-depth interviews with healthcare providers. Healthcare providers (cardiologists, internists, cardiac and critical care nurses, critical care specialists, and general practitioners) were purposively selected through maximum variation sampling from ten hospitals in four regions of Ethiopia. Data were transcribed verbatim, coded inductively, and analyzed thematically. The data analysis identified six themes: perceived benefit of AI-powered ECG interpretation CDSS, trust development, workflow integration, ethical concerns, functionality, and adoption determinants. Participants emphasized AI’s potential to enhance accessibility, consistency, and diagnostic accuracy while reducing subjectivity and unnecessary referrals. Acceptance relied on high accuracy, reliable data, and rigorous validation, with the technology seen as supportive rather than replacing clinicians. Material resources, human resource readiness, and leadership engagement were key factors for adoption. Recommendations included phased implementation, continuous training, and model expansion to ensure sustainability and clinical utility. The AI-powered ECG interpretation CDSS was viewed as a valuable adjunct for strengthening cardiovascular care in Ethiopia, highlighting the need for context-sensitive strategies, ethical safeguards, and multi-level system readiness for successful adoption. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

Inflammatory bowel disease (IBD)—including Crohn’s disease, ulcerative colitis, and indeterminate colitis—is a chronic immune-mediated condition that primarily affects the intestinal mucosa but often presents with extraintestinal digestive manifestations, which are important yet frequently underrecognized sources of morbidity. These heterogeneous manifestations reflect diverse genetic, microbial, immunologic, and environmental influences, highlighting the value of a personalized medicine approach. Hepatobiliary involvement affects IBD adults patients and is even more common in children, ranging from mild liver enzyme elevations to severe complications such as liver failure, with autoimmune disorders, cholelithiasis, portal vein thrombosis, and non-alcoholic fatty liver disease as key considerations. Pancreatic manifestations may include autoimmune or acute pancreatitis, often linked to gallstones, thiopurine exposure, or duodenal Crohn’s disease, while splenic abnormalities, such as granulomatous lesions, splenomegaly, or functional hyposplenism, reflect systemic immune dysregulation. Oral findings—including aphthous ulcers, periodontitis, pyostomatitis vegetans, and granulomatous cheilitis—can serve as early, patient-specific indicators of disease activity. Personalized approaches, encompassing investigations tailored to the individual profile and selected targeted therapies, are essential for improving diagnostic accuracy, preventing complications, and optimizing multidisciplinary care in patients with IBD. Full article

The demand for advanced detection methods and accurate staging remains a global challenge in cancer diagnosis. Even though traditional deep learning models in medical imaging achieve high precision, they suffer from limited explainability and multimodal reasoning due to their black-box nature, thereby limiting their clinical applicability. To address this gap, recent research has increasingly explored multimodal approaches that integrate visual and textual clinical data to enhance diagnostic accuracy and interpretability. This study presents a bibliometric analysis of 408 publications from 2021 to 2025, collected from Web of Science and Scopus, using VOSviewer and R-Bibliometrix to map citation networks, co-authorship, and keyword co-occurrences. The results reveal a rapid growth from 1 publication in 2021 to 269 in 2025, with significant contributions from leading countries and institutions. Thematic analysis indicates a shift from conventional convolutional approaches toward transformer-based and self-supervised methods, alongside increasing attention to multimodal learning in cancer imaging tasks such as breast, lung, and brain cancer analysis. Overall, this study provides a structured overview of the evolving research landscape, highlighting key trends, emerging themes, and research gaps to inform future developments in multimodal artificial intelligence for cancer diagnosis. Full article

In the fault diagnosis of complex industrial systems, labeled samples are expensive to obtain, which leads to insufficient training data for the belief rule base (BRB) model. Although unlabeled samples are abundant, the uncertainty of their pseudo-labels may undermine semi-supervised learning and hinder accurate parameter optimization of the BRB model. To address these issues, a dual-source evidence-driven semi-supervised BRB method (SS-BRB) is proposed for fault diagnosis. The proposed method makes effective use of unlabeled samples while preserving the interpretability and inference transparency of the BRB model. To improve the reliability of pseudo-labels in semi-supervised learning, a dual-source evidence-driven pseudo-labeling mechanism is designed. In this mechanism, local similarity information is combined with the global inference results of the BRB model. An entropy factor and a feature distance factor are introduced to adaptively adjust the confidence of pseudo-labels. In this way, the quality of pseudo-labels is improved, and the influence of noisy samples is reduced. Based on this mechanism, high-confidence pseudo-labeled samples are incorporated into the training set to further optimize the model. Experimental results show that the proposed method achieves good diagnostic performance on both the gearbox dataset and the WD615 diesel engine dataset. Even with limited labeled data, the proposed method still achieves high accuracy, robustness, and good generalization performance. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

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

Background: Cysteine proteases of Clonorchis sinensis are potential diagnostic antigens, yet the performance of individual members within this diverse enzyme family requires systematic evaluation. This study aimed to assess the diagnostic potential of four recombinant cysteine proteases (rCsCP1–4) for human clonorchiasis. Methods: An indirect ELISA was developed to measure serum reactivity (IgG, IgG subclasses, IgA) against rCsCP1–4. The assay was validated using 180 microscopy-confirmed positive and 148 negative control sera. Samples were randomly split into training and validation sets (7.5:2.5). Diagnostic performance of single antigens and their combinations was evaluated using univariate and multivariate logistic regression and compared with a commercial kit. Key metrics included the area under the curve (AUC), sensitivity, specificity, accuracy, F1-score, and Kappa coefficient. Results: Four single antigen–antibody pairs showed high performance: rCsCP1-IgG4 (AUC = 0.928), rCsCP2-IgA (AUC = 0.863), rCsCP3-IgG1 (AUC = 0.920), and rCsCP4-IgG4 (AUC = 0.958). Among these, rCsCP1-IgG4, rCsCP3-IgG1, and rCsCP4-IgG4 outperformed the commercial kit, achieving higher sensitivity (92.0%, 96.0%, 96.0% vs. 86.0%), specificity (87.5%, 81.3%, 90.6% vs. 78.1%), accuracy (92.0%, 88.9%, 94.1% vs. 86.0%), and F1-scores (0.902, 0.902, 0.939 vs. 0.829). The Kappa values for rCsCP1-IgG4 (0.768) and rCsCP4-IgG4 (0.773) indicated substantial agreement with the microscopic standard. Multi-antigen combinations (triple or quadruple) further enhanced performance, achieving sensitivity and specificity > 98% with an AUC approaching 1.0. Conclusions: This study identifies rCsCP1 and rCsCP4, particularly in combination with IgG4 detection, as highly promising diagnostic targets for clonorchiasis. Multi-antigen combinations significantly improved diagnostic performance compared to single-antigen assays, offering a strategy for high-precision diagnosis. Furthermore, the efficacy of the rCsCP2-IgA pair suggests that detecting fecal secretory IgA could be a novel avenue for non-invasive, self-testing applications. Full article

This study aims to improve the process of characterizing oil on the sea surface using synthetic aperture radar (SAR) imagery, seeking to increase the accuracy of oil slick classification as natural or anthropogenic. A set of spatial attributes was obtained using semivariograms and phase-space pictures. This novel approach demonstrated potential to add value for monitoring seepage phenomena, which is of great scientific and environmental importance. The results achieved have potential for operational application as an aid in understanding active petroleum systems, reducing exploration risk in the decision-making process. Different targets display semivariograms with distinct geostatistical parameters, thus expressing contrasting models of spatial data correlation. The research results indicate that trajectories developed by the targets “sea”, “seepage slick”, and “oil spill” showed diagnostic behavior in their respective phase-space pictures. Full article

In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), directly affects vehicle performance and the total cost of ownership of electric vehicles. This review article systematizes and analyzes current approaches to assessing the technical condition of battery packs. Fundamental degradation mechanisms and factors are considered, including operational, thermal, and mechanical effects. A detailed analysis is presented for the three main classes of diagnostic methods: model-based approaches, data-driven approaches (machine learning and deep learning), and hybrid methods combining the advantages of the former two. Particular attention is paid to methods for early fault detection, thermal runaway prediction, and condition assessment based on real-world operational data. The article presents quantitative results demonstrating the accuracy and effectiveness of various algorithms and also discusses key challenges and promising research directions, such as the use of cloud platforms, digital twins, and explainable artificial intelligence methods. Full article

Background/Objectives: The diagnosis of endometriosis (EM) remains challenging due to the lack of a perfect diagnostic standard and the poor concordance between clinical symptoms and lesion severity. Although laparoscopy is widely used in clinical practice, it is invasive and associated with a non-negligible false-negative rate, while serum CA125 has limited diagnostic accuracy. In our previous studies, circulating endometrial cells (CECs) were identified in the peripheral blood of patients with EM, suggesting their potential as a non-invasive biomarker. Building on this finding, the present study aimed to systematically evaluate the clinical value of CECs in the diagnosis and stratified diagnosis of EM in the absence of a perfect diagnostic reference standard. Methods: Female patients treated at the Department of Obstetrics and Gynecology, Peking University People’s Hospital, between June 2022 and June 2024 were enrolled. Participants were clinically classified according to laparoscopic evaluation into an EM group and a non-EM group. However, laparoscopy was not treated as a definitive diagnostic gold standard in the statistical analysis. Instead, given the absence of a perfect reference standard, nonparametric latent class analysis was applied to jointly estimate disease status and the diagnostic performance of CECs, CA125, and laparoscopy. Patients with EM were further stratified according to dysmenorrhea severity (mild, moderate, and severe), lesion activity status (active or dormant), and menstrual cycle phase. Peripheral blood samples were collected from all participants, and CECs were detected using subtraction enrichment combined with immunofluorescence and fluorescence in situ hybridization (SE-iFISH). Serum CA125 levels were measured concurrently. Results: A total of 302 participants were included. The primary analysis focused on 133 surgically confirmed EM patients and 146 non-EM controls. After adjustment for an imperfect diagnostic reference standard, CECs demonstrated superior diagnostic performance compared with serum CA125 in the overall cohort, with higher sensitivity (0.58 vs. 0.37) and specificity (0.81 vs. 0.75). Under laparoscopic assessment in patients with severe dysmenorrhea (VAS ≥ 7), where the sensitivity and specificity were 0.759 and 1.00, respectively, CECs demonstrated superior diagnostic performance compared with serum CA125, with higher sensitivity (0.694 vs. 0.355) and specificity (0.946 vs. 0.429). Similarly, in patients with active EM, where laparoscopy showed a sensitivity of 0.79 and a specificity of 1.00, CECs again demonstrated superior diagnostic performance compared with CA125 (sensitivity 0.73 vs. 0.35; specificity 0.96 vs. 0.31), showing high concordance with laparoscopic diagnosis. When stratified by menstrual cycle phase, CECs maintained superior diagnostic performance over CA125 during both the proliferative and menstrual phases, with higher sensitivity (0.84 vs. 0.44) and specificity (0.83 vs. 0.65). Conclusions: Circulating endometrial cells (CECs) demonstrate high diagnostic accuracy for EM, significantly outperforming serum CA125, and show high concordance with laparoscopic diagnosis across clinically relevant stratified conditions in the absence of a perfect diagnostic gold standard. Full article

Background/Objectives: Breast cancer continues to be one of the most serious and common afflictions affecting women around the globe. Despite ultrasound imaging being an effective method for the detection of abnormalities in dense breast tissues, there are a number of drawbacks when utilizing this method, including the subjective nature of the imaging and the variant nature of the imaging due to the cognitive biases of the interpreting expert and the experience of the interpreting expert. The above factors are the cause of the increased need in the implementation of AI-driven models for diagnostic analysis. In this research, we provide a hybrid deep learning-based framework for cancer classification of the breast cancer ultrasound image dataset (‘BUSI dataset’). Methods: The contributing models of the proposed architecture involve the combination of a light ViT encoder and an EfficientNetV2-RW-S feature extractor. The combination mentioned leverage the positive sensitivities of the convolutional neural networks (CNNs) and the global reasoning neural networks (i.e., transformers) in the explanation of the architecture. The reason being, EfficientNetV2 diminishes the capture of the fine-grained morphological components of the lesions, edges, and echogenic variances of the tissue, whereas the transformer model diminishes the long-range dependencies of the lesions and other surrounding tissues. Results: The experimental results from the proposed hybrid model of the architecture demonstrates an enhanced classification accuracy of 97.95%, in contrast to the self-standing models of the architecture, the hybrid model supersedes the isolated ViT model (i.e., 89%) and the isolated CNN model (i.e., 80%) frameworks. Furthermore, the proposed model hybrid architecture also diminishes the overall self-attention computational complexity of the proposed model by substantially diminishing the number of tokens reaching an overall count of 10 (from the vast 197 tokens). This further leads to a substantial decrease in the memory and cost expended during the attention processes. Conclusion: Overall, this study proposes a method for the improved diagnostic and computational analysis, suggesting the proposed architecture to be a potential framework for use in the contemporary clinical environments. Full article

Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-image conversion technique, leading to insufficient information dimensionality and inadequate feature representation, which ultimately limits diagnostic accuracy. To address these challenges, this paper proposes a bearing fault diagnosis method (GADF-MTF-RP-VGG16, GMRVGG) based on tri-modal image feature fusion. Specifically, three image conversion techniques—Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP)—are utilized to first convert 1D vibration signals into 2D images. Subsequently, shallow to deep features are extracted and fused through the VGG16 backbone network. Finally, fault diagnosis is achieved by integrating a fully connected classifier layer. The proposed methodology was comprehensively validated on both the Case Western Reserve University (CWRU) and the University of Ottawa datasets, which were augmented with severe 6 dB Gaussian white noise and 6 dB pink noise to simulate complex industrial environments. Under these harsh conditions, the proposed method achieved superior overall accuracies (up to 96.9% on the CWRU dataset and consistently 95.8% on the Ottawa dataset), significantly surpassing conventional single-modal approaches. This effectively addresses the limitations of insufficient feature dimensionality and inadequate representation, establishing a highly reliable and robust solution for intelligent bearing fault diagnosis. Full article

Rolling bearing fault diagnosis under complex and noisy operating conditions requires not only high diagnostic accuracy but also interpretability that can be quantitatively verified against physically meaningful excitation structures. However, many existing deep learning approaches rely on a single time–frequency (TF) representation and provide limited, non-verifiable links between model decisions and the original vibration patterns. To address this issue, we propose MBT-XAI, a multi-wavelet TF fusion network with a Token-to-Spectrum Traceback (TST) mechanism for structure-preserving, physics-consistent interpretability. Three complementary wavelets, namely Morlet, Mexican Hat, and Complex Morlet, are used to construct multi-view TF representations, which are encoded into RGB channels and adaptively fused via cross-channel attention within a Transformer backbone. TST maps patch-token attributions back to the TF domain, enabling quantitative evaluation of physics consistency through overlap-based metrics. Experiments on the public CWRU dataset and an industrial IMUST dataset show that MBT-XAI achieves 98.13 ± 0.24% and 96.23 ± 0.31% accuracy at SNR = 0 dB, outperforming the strongest baseline by 2.83% and 2.43%, respectively. Under AWGN contamination, MBT-XAI maintains 95.44 ± 0.38%/93.45 ± 0.47% accuracy on CWRU and 95.80 ± 0.33%/92.91 ± 0.51% accuracy on IMUST at SNR = −2/−4 dB. Under colored-noise contamination, the proposed method also preserves robust performance under pink and brown noise at the same SNR levels. Quantitative interpretability evaluation further indicates high alignment between salient frequency regions and theoretical fault-characteristic bands, with IoU = 80.21 ± 0.86% and Coverage = 91.70 ± 0.63%. In addition, MBT-XAI requires 10.393 M parameters and 10.678 GFLOPs, with an inference latency of 14.7 ms per sample (batch size = 1) on an NVIDIA GeForce RTX 3060 GPU. These results suggest that multi-wavelet TF modeling with attention-based fusion and TF-level traceback provides an accurate, robust, and physics-consistent framework for intelligent bearing fault diagnosis. Full article