Search Results (4,748)

Background: To explore the correlation between spatial dose distribution and post-implant urinary toxicity, aiming to assist decision making in low-dose-rate (LDR) treatment planning, thereby improving patient outcomes. Methods: Eighty-five prostate LDR patients with >12-month follow-up were included. Patient-reported urinary toxicity was collected prospectively using the International Prostate Symptom Score (IPSS) questionnaire, from before implant (baseline) to post-implant follow-up. Patients were then grouped into those whose symptom scores returned to ≤2 points above baseline by 12 months (no long-term toxicity) vs. those who did not (long-term toxicity). A total of 106 features were extracted for each patient, including principal components of dose-volume histograms (DVHs) from multiple prostate subzones, the whole prostate and urethra, along with baseline IPSS, implantation characteristics, and additional DVH indicators for the prostate and the urethra. A machine learning (ML) model incorporating backward feature selection algorithm was developed to predict long-term toxicity status, using a shuffle-and-split validation strategy for model evaluation during feature selection. A univariate statistical analysis was conducted on the model’s selected features. Results: Out of 85 patients, 41 (48%) had long-term urinary toxicity. Seven features were selected during model training, including baseline IPSS and six dosimetric features from several prostate subzones primarily located in the posterior prostate. The model achieved a high mean area under the receiver operating characteristic curve (AUC) of 0.81, with a balanced sensitivity and specificity of 0.78 by adjusting the probability threshold. In univariate analysis, only baseline IPSS and one selected dose feature were significantly correlated with long-term toxicity with AUC < 0.71. Conclusions: The proposed ML model, integrating baseline IPSS and spatial dosimetric features, effectively predicts long-term urinary toxicity after prostate LDR. This approach offers a practical method for risk stratification, allowing clinicians to identify patients at elevated risk and prioritize them for targeted preventative measures and closer follow-up. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management. Full article

Soiling in photovoltaic systems is a recurring problem that reduces energy generation and demands efficient operation and maintenance (O&M) strategies. In this context, this paper proposes a machine learning-based approach to identify dirt levels and generate cleaning alerts using operational and weather data. Initially, the models were evaluated with a decision threshold ranging from 0.5 to 0.7, using only operational features. Subsequently, the inclusion of weather features was tested, which improved the models’ performance and enabled the selection of the best models for the exhaustive features search step. The models analyzed in this step were Extra Trees, Histogram-based Gradient Boosting, Extreme Gradient Boosting, and Random Forest. Exhaustive analysis further improved model performance, as indicated by global metrics and ROC curves. The Extra Trees model with a threshold of 0.5 showed the best performance and was selected as the final configuration, achieving an accuracy of 0.9884 and an AUC-ROC of 0.9957. Finally, the selected model was applied to determine daily soiling levels and trigger alerts based on temporal persistence, indicating its potential to support predictive O&M decisions and cleaning actions in PV systems. Full article

Background/Objectives: Electroanatomic mapping (EAM) provides high-resolution spatial and electrogram information, but the prognostic utility of quantitative EAM features has not been systematically evaluated with contemporary artificial intelligence (AI) methods. We investigated whether an AI analysis of quantitative EAM exports from the CARTO system enhances the prediction of major arrhythmic events (MAEs). Methods: In this retrospective, multicenter cohort study, 248 consecutive patients undergoing left ventricular EAM at four tertiary electrophysiology centers were analyzed. Numerical EAM descriptors (spatial coordinates, unipolar/bipolar voltages, local activation time, impedance) were transformed into derived metrics, including local activation heterogeneity (GR), late-potential extent (LAT), bipolar–unipolar discrepancy (VLT), and low-amplitude scar extent (Scar Areas), and were spatially normalized via spherical projection. Clinical, anamnestic, and imaging variables were integrated. Machine learning and deep learning models were trained with an 80:20 train/test split and evaluated using three-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision. Results: Models incorporating both clinical and AI-processed EAM features achieved high discriminatory performance (test AUC up to 0.92; accuracy up to 0.896). Specificity was consistently high (≈0.97–0.998), whereas sensitivity remained modest (≈0.39–0.58). Among the EAM-derived features, GR was the most consistently informative predictor across algorithms and analyses; VLT, LAT, and Scar Areas also contributed substantially. Regionally, basal sub-mitral, subaortic, and posterolateral basal-to-mid zones exhibited the strongest associations with MAEs. Conclusions: AI-driven quantitative analysis of left ventricular EAM exports augments risk stratification for MAEs beyond conventional clinical and binary EAM descriptors. Reflecting local conduction heterogeneity, GR emerged as the dominant EAM predictor. Prospective validation in larger, disease-specific cohorts and real-time integration within EAM platforms are warranted. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

Cardiovascular disease (CVD) risk assessment in older adults requires models that are accurate, clinically interpretable, and able to retain performance in independent populations. This study developed an interpretable machine-learning framework for CVD risk stratification in individuals aged 65 years and older using routinely available clinical factors and a selected biochemical extension and then evaluated its performance in a substantially larger independent external cohort. Model development used a development cohort of 100 patients (Almaty, age ≥ 65) with leakage-free nested cross-validation and out-of-fold (OOF) probabilities. Three internally evaluated configurations were compared: a clinical logistic regression baseline (LR clinical), a biomarker-augmented logistic regression (LR selected), and a nonlinear random forest on the selected feature set (RF selected). Discrimination was assessed using ROC-AUC and PR-AUC; probabilistic accuracy using Brier score and log loss. Calibration was examined using OOF calibration curves with sigmoid calibration for selected models. Decision-analytic utility and exploratory operational thresholds were assessed using Decision Curve Analysis (DCA), yielding a three-tier scale with thresholds t__low = 0.23 and t__high = 0.40. In nested cross-validation, LR clinical achieved ROC-AUC 0.9425 ± 0.0188 and PR-AUC 0.9574 ± 0.0092 with Brier 0.1004 ± 0.0215 and log loss 0.3634 ± 0.0652; LR selected performed worse, while RF selected showed competitive discrimination. External validation on an independent cohort (n = 695) showed retained discrimination (ROC-AUC 0.8355; PR-AUC 0.9376) with acceptable probabilistic accuracy (Brier 0.1131; log loss 0.3760), and recalibration (intercept + slope) slightly improved probability metrics. Explainability analyses (odds ratios, permutation importance, SHAP) consistently identified heredity, BMI, physical activity, and diabetes as influential model-associated factors, with clinically plausible directionality. The results suggest that an interpretable model trained on a small geriatric cohort can retain meaningful predictive performance on a substantially larger external cohort, supporting the potential value of transparent risk stratification in older adults, while broader prospective and multi-center validation remains necessary before routine clinical implementation. Full article

Background: Magnetic resonance imaging (MRI) is the reference standard for evaluating temporomandibular joint (TMJ) disorders, particularly for assessing disc position, joint effusion, and degenerative changes. With increasing imaging demands and advances in deep learning, artificial intelligence (AI) has emerged as a potential adjunct to expert interpretation. This systematic review aimed to compare the diagnostic performance of AI-based models with that of human experts in TMJ MRI analysis. Methods: This review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251174127). A systematic search of PubMed/MEDLINE, ScienceDirect, Wiley Online Library, and Springer Nature Link was performed for studies published between 2020 and 2026. Eligible studies included human participants undergoing TMJ MRI and evaluated AI, machine learning, or deep learning models against human expert interpretation. Extracted outcomes included sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and agreement metrics. Risk of bias was assessed using QUADAS-2. Due to substantial heterogeneity, a narrative synthesis was conducted. Results: Five retrospective diagnostic accuracy studies were included, comprising sample sizes ranging from 118 to 1474 patients. Target conditions included anterior disc displacement, joint effusion, osteoarthritis, and disc perforation. AI models demonstrated strong discriminative performance, with reported AUC values ranging from 0.79 to 0.98. In direct comparisons, AI achieved diagnostic accuracy comparable to experienced radiologists. AI systems frequently demonstrated higher specificity and similar overall accuracy, whereas human experts often showed higher sensitivity. In osteoarthritis assessment, AI performance approached expert level and exceeded that of less experienced readers. All studies were retrospective and predominantly single-center, with heterogeneous reference standards and limited external validation. Conclusions: AI achieves diagnostic performance comparable to experienced clinicians in TMJ MRI interpretation and shows promise as a decision-support tool. Nevertheless, it should be regarded as complementary to, rather than a replacement for, expert radiological assessment pending further rigorous validation. Full article

Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ_s′, with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ_s′, a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

Peen forming is a dieless manufacturing process used to shape large, thin aerospace panels through controlled shot impacts that induce residual stresses and curvature. Despite long-standing industrial use, process monitoring still depends largely on indirect proxies such as Almen intensity and coverage, limiting spatially resolved deformation assessment and hindering closed-loop control. In parallel, vision-based artificial intelligence (AI) has enabled adaptive monitoring and feedback in smart-manufacturing domains such as welding, additive manufacturing, and sheet forming. This review examines how such sensing and learning strategies can be transferred to adaptive peening forming. We compare six vision sensing modalities and assess major AI model families for surface mapping, temporal prediction, robustness, and deployment maturity. The synthesis shows that progress is primarily constrained by limited validated datasets, harsh in-cabinet sensing conditions, scarce closed-loop demonstrations, and weak validation on curved aerospace geometries. We conclude that the sensing and AI foundations for adaptive peen forming are already emerging, but industrial translation now depends on stronger experimental validation, standardized benchmarking, robust multi-sensor integration, and edge-capable feedback pipelines. Full article

In the treatment of atopic dermatitis (AD), synergistic activation of the aryl hydrocarbon receptor (AHR)/nuclear factor erythroid 2-related factor 2 (NRF2) pathways represents a promising strategy. However, known dual agonists are limited, and traditional screening methods are inefficient. Therefore, this study developed machine learning models to predict AHR/NRF2 dual agonists using molecular descriptors and fingerprints. All models achieved area under the receiver operating characteristic curve (AUC) values above 0.86, indicating good classification performance. The optimal AHR model showed an accuracy (ACC) of 0.811 and an AUC of 0.878, while the best NRF2 model yielded an ACC of 0.839 and an AUC of 0.907. Based on this model, compounds with a low fraction of sp³-hybridized carbons, moderate hydrophobicity, limited alkyl chains, and highly conjugated structures tend to act as AHR/NRF2 dual agonists. Finally, this study screened 1011 potential natural AHR/NRF2 dual agonists suitable for drug development. Among these, 2-arylbenzofurans, alkaloids, phenanthrenes, flavones, and furocoumarins demonstrated particular advantages. For validation, Indirubin, imperatorin and 3′-O-Methylbutastatin III were first discovered as AHR/NRF2 dual agonists in HaCaT cells. This work provides a robust predictive tool, clarifies key molecular features of dual agonists, and may support the discovery of anti-AD agents. Full article

Early diagnosis of cognitive decline is vital for timely treatment of mild cognitive impairment (MCI) and Alzheimer’s disease (AD), yet standard clinical assessments often miss subtle longitudinal language changes. We propose a hierarchical hybrid intelligence framework integrating long-context language modeling, temporal progression, semantic graph reasoning, psycholinguistic biomarkers, and contrastive progression learning to classify patient states (Normal, MCI, AD) from longitudinal electronic health record (EHR) notes. The model was trained on 4500 patients and 68,000 clinical notes from Medical Information Mart for Intensive Care III (MIMIC-III) and externally validated on the Medical Information Mart for Intensive Care IV (MIMIC-IV) clinical notes dataset (5200 patients, 72,000 notes). Inputs combined Biomedical and Clinical Bidirectional Encoder Representations from Transformers (BioClinicalBERT) embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM) temporal encodings, Graph Sample and Aggregate (GraphSAGE)-based Unified Medical Language System (UMLS) concept graphs, and psycholinguistic vectors (lexical diversity, grammatical complexity, discourse coherence). On the MIMIC-III hold-out set, the model achieved 99.999% accuracy, a macro F1-score of 0.999, a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.999, and a temporal stability variance of 0.0008. Monte Carlo cross-validation (10,000 folds) yielded $99.997\pm 0.003$% accuracy and $0.999\pm 0.001$ macro F1. Feature ablation confirmed distinct gains from temporal, semantic, and psycholinguistic modules, improving performance by 1.1% over text-only baselines. Cross-cohort zero-shot testing on MIMIC-IV showed strong generalization with minimal decline in macro F1 and balanced accuracy. Explainability analyses, such as SHapley Additive exPlanations (SHAP) token/concept attribution, attention maps, counterfactual perturbations, and psycholinguistic importance, revealed clinically interpretable markers, such as pronoun overuse, reduced lexical diversity, and syntactic simplification, as predictors of decline. Our framework supports scalable, non-invasive early screening in a variety of healthcare settings by providing longitudinally stable predictions. Full article

Background/Objectives: Bipolar disorder (BD) is one of the leading causes of disability worldwide, causing significant functional impairments in those affected. The heterogeneous course of BD renders the prediction of clinical progress and outcomes challenging, but it can be potentially enhanced with the use of artificial intelligence methods. In this systematic review, we aimed to examine the extant literature regarding the predictive accuracy of clinical functioning, illness affective state, relapse, and relevant predictors amongst patients with BD, using artificial intelligence methods. Methods: The study was guided by PRISMA and the Cochrane Handbook for Systematic Reviews. Six electronic databases were systematically searched from inception for relevant studies until July 2025 and relevant data were summarised in tables. The protocol of the review was registered on Prospero, ID: CRD42024590343. Results: Forty articles were included in this review. The area under the curve (AUC) values for clinical functioning, illness affective state, and relapse prediction were 0.59–0.72 (poor to acceptable), 0.57–0.97 (poor to outstanding), and 0.45–0.98 (poor to outstanding), respectively. Supervised, tree-based algorithms performed the best. Predictive factors included sociodemographic, clinical and psychological factors and wearable data, as well as speech and video recordings. Conclusions: Existing studies showed the potential of machine learning methods in the prediction of clinical progress and outcomes of BD (specifically functional status, affective state, and relapse) based on relevant collected variables. Longitudinal studies can further clarify and validate the associated predictive factors for earlier identification of those at risk of poorer prognosis to enhance management of BD. Full article

Against the backdrop of the global energy structure accelerating its transition towards a clean and low-carbon model, rooftop-distributed photovoltaic (PV) systems are playing an increasingly prominent strategic role in urban energy supply systems, owing to their notable advantages such as environmental friendliness and high spatial utilization efficiency. Consequently, they are becoming a critical pillar in advancing urban energy transformation and enhancing sustainable development. This paper aims to explore deep learning-based techniques for assessing the potential of large-scale distributed PV installations. To accurately evaluate their dynamic power generation capability, a hybrid prediction model integrating variational mode decomposition (VMD), the mutual information (MI) method, and a cascaded xLSTM-Informer network is proposed. Firstly, the model preprocesses key meteorological sequences using VMD, decomposing them into modal components of different frequencies. Subsequently, the MI method is employed to extract critical sequences. Then, the xLSTM module is utilized to learn the long-term complex dependencies between meteorological conditions and PV power output, while the Informer network captures key global temporal patterns, achieving high-precision time-series forecasting of PV generation. Finally, employing the forecasted time-series power curve as the core input, a comprehensive analytical framework for PV installation potential is constructed, integrating assessments of technical feasibility, economic viability, and environmental performance. This framework aims to scientifically estimate the admissible installed capacity and system value of distributed PV systems, thereby providing a dynamic basis for decision-making in urban planning. Full article