Search Results (564)

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and multi-task learning paradigms. We systematically categorize and analyze existing technologies based on sensing modalities (vision-based, radar-based, thermal imaging, and ambient sensing), integration strategies, and application domains. The paper examines how artificial intelligence has revolutionized this domain, transitioning from early single-modality, single-parameter approaches to sophisticated systems that combine complementary sensing technologies and simultaneously extract multiple vital sign parameters. We discuss the theoretical foundations and practical implementations of multi-modal fusion, analyzing signal-level, feature-level, decision-level, and deep learning approaches to sensor integration. Similarly, we explore multi-task learning frameworks that leverage the inherent relationships between vital sign parameters to enhance measurement accuracy and efficiency. The review also critically addresses persisting technical challenges, clinical limitations, and ethical considerations, including environmental robustness, cross-subject variability, sensor fusion complexities, and privacy concerns. Finally, we outline promising future directions, from emerging sensing technologies and advanced fusion architectures to novel application domains and privacy-preserving methodologies. This review provides a holistic perspective on contactless vital sign monitoring, serving as a reference for researchers and practitioners in this rapidly advancing field. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments. Full article

Background/Objectives: Aging is often accompanied by physical and cognitive decline, affecting older adults’ mobility. Virtual reality (VR) offers innovative opportunities to safely practice everyday tasks, such as street crossing. This study was designed as a feasibility and pilot study to explore acceptance, usability, and preliminary effects of a VR-based road-crossing intervention for older adults. It investigates the use of virtual reality (VR) as an innovative training tool to support senior citizens in safely navigating everyday challenges such as crossing roads. By providing an immersive environment with realistic traffic scenarios, VR enables participants to practice in a safe and controlled setting, minimizing the risks associated with real-world road traffic. Methods: A VR training application called “Wegfest” was developed to facilitate targeted road-crossing practice. The application simulates various scenarios commonly encountered by older adults, such as crossing busy streets or waiting at traffic lights. The study applied a single-group pre-post design. Outcomes included the Timed Up and Go test (TUG), Falls Efficacy Scale-International (FES-I), and Montreal Cognitive Assessment (MoCA). Results: The development process of “Wegfest” demonstrates how a highly realistic street environment can be created for VR-based road-crossing training. Significant improvements were found in the Timed Up and Go test (p = 0.002, d = 0.784) and fall-related self-efficacy (FES-I, p = 0.005). No change was observed in cognitive function (MoCA, p = 0.56). Participants reported increased subjective safety (p < 0.001). Discussion: The development of the VR training application “Wegfest” highlights the feasibility of creating realistic virtual environments for skill development. By leveraging immersive technology, both physical and cognitive skills required for road-crossing can be effectively trained. The findings suggest that “Wegfest” has the potential to enhance the mobility and safety of older adults in road traffic through immersive experiences and targeted training interventions. Conclusions: As an innovative training tool, the VR application not only provides an engaging and enjoyable learning environment but also fosters self-confidence and independence among older adults in traffic settings. Regular training within the virtual world enables senior citizens to continuously refine their skills, ultimately improving their quality of life. Full article

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches still suffer from limited generalization and suboptimal accuracy. To address these challenges, this study proposes an improved rice pest detection model, MTD-YOLO, based on the YOLOv8 framework. First, the original backbone is replaced with MobileNetV3, which leverages optimized depthwise separable convolutions and the Hard-Swish activation function through neural architecture search, effectively reducing parameters while maintaining multiscale feature extraction capabilities. Second, a Cross Stage Partial module with Triplet Attention (C2f-T) module incorporating Triplet Attention is introduced to enhance the model’s focus on infested regions via a channel-patial dual-attention mechanism. In addition, a Dynamic Head (DyHead) is introduced to adaptively focus on pest morphological features using the scale–space–task triple-attention mechanism. The experiments were conducted using two datasets, Rice Pest1 and Rice Pest2. On Rice Pest1, the model achieved a precision of 92.5%, recall of 90.1%, mAP@0.5 of 90.0%, and mAP@[0.5:0.95] of 67.8%. On Rice Pest2, these metrics improved to 95.6%, 92.8%, 96.6%, and 82.5%, respectively. The experimental results demonstrate the high accuracy and efficiency of the model in the rice pest detection task, providing strong support for practical applications. Full article

Urban green spaces (UGSs) and forests play a vital role in shaping sustainable and livable cities, offering not only ecological benefits but also spaces that are essential for human well-being, social interactions, and everyday life. Understanding the landscape features that resonate most with public preferences is essential for enhancing the appeal, accessibility, and functionality of these environments. However, traditional approaches—such as surveys or single-data analyses—often lack the nuance needed to capture the complex and multisensory nature of human responses to green spaces. This study explores a cross-modal methodology that integrates natural language processing (NLP) and deep learning techniques to analyze text and image data collected from public reviews of 19 urban parks in Nanjing. By capturing both subjective emotional expressions and objective visual impressions, this study reveals a consistent public preference for natural landscapes, particularly those featuring evergreen trees, shrubs, and floral elements. Text-based data reflect users’ lived experiences and nuanced perceptions, while image data offers insights into visual appeal and spatial composition. By bridging human-centered insights with data-driven analysis, this research provides a robust framework for evaluating landscape preferences. It also underscores the importance of designing green spaces that are not only ecologically sound but also emotionally resonant and socially inclusive. The findings offer valuable guidance for the planning, design, and adaptive management of urban green infrastructure in ways that support healthier, more responsive, and smarter urban environments. Full article

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible. Full article

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R² = 0.47), whereas k-NN attained 10.8 years (R² = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption. Full article

The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening. Full article

Background: Leigh’s Disease is a rare mitochondrial disorder primarily affecting the central nervous system, with frequent secondary cardiac manifestations such as hypertrophic and dilated cardiomyopathies. Early detection of cardiac complications is crucial for patient management, but manual interpretation of cardiac MRI is labour-intensive and subject to inter-observer variability. Methodology: We propose an integrated deep learning framework using cardiac MRI to automate the detection of cardiac abnormalities associated with Leigh’s Disease. Four CNN architectures—Inceptionv3, a custom 3-layer CNN, DenseNet169, and EfficientNetB2—were trained on preprocessed MRI data (224 × 224 pixels), including left ventricular segmentation, contrast enhancement, and gamma correction. Morphological features (area, aspect ratio, and extent) were also extracted to aid interpretability. Results: EfficientNetB2 achieved the highest test accuracy (99.2%) and generalization performance, followed by DenseNet169 (98.4%), 3-layer CNN (95.6%), and InceptionV3 (94.2%). Statistical morphological analysis revealed significant differences in cardiac structure between Leigh’s and non-Leigh’s cases, particularly in area (212,097 vs. 2247 pixels) and extent (0.995 vs. 0.183). The framework was validated using ROC (AUC = 1.00), Brier Score (0.000), and cross-validation (mean sensitivity = 1.000, std = 0.000). Feature embedding visualisation using PCA, t-SNE, and UMAP confirmed class separability. Grad-CAM heatmaps localised relevant myocardial regions, supporting model interpretability. Conclusions: Our deep learning-based framework demonstrated high diagnostic accuracy and interpretability in detecting Leigh’s disease-related cardiac complications. Integrating morphological analysis and explainable AI provides a robust and scalable tool for early-stage detection and clinical decision support in rare diseases. Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article

This paper presents the Integrated Energy Community Performance Index (IECPI), a novel multi-criteria evaluation framework designed to assess the systemic performance of energy communities (ECs) across environmental, technological, social, and economic/institutional dimensions. Although ECs are increasingly recognized as pivotal actors in the decentralized energy transition, the absence of integrated assessment tools continues to hinder comparability, strategic planning, and long-term monitoring. The IECPI addresses this critical gap by structuring performance evaluation around nine normalized indicators, with their respective weights empirically derived from an influence matrix calibrated using interdependencies identified in 60 documented case studies. The IECPI integrates both objective and subjective metrics, capturing measurable outcomes alongside governance structures and contextual factors. The results reveal significant disparities in the performance of energy communities, allowing for the identification of five strategic typologies: Technologically Driven, Environmentally Oriented, Socially Embedded, Balanced Performance, and Structurally Fragile. The IECPI facilitates benchmarking, targeted policymaking, and cross-case learning while aligning with international frameworks such as SDG 7, EMAS, and principles of inclusive governance. As a scalable and transferable model, it provides a robust foundation for evidence-based planning, the evaluation of community resilience, and sustainability-oriented decision-making within distributed energy systems. Full article