Search Results (274)

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

Mammary Paget disease (MPD) is a rare cutaneous malignancy associated with underlying ductal carcinoma in situ (DCIS) or invasive ductal carcinoma (IDC). Clinically, it appears as eczematous changes in the nipple and areola complex (NAC), which may include itching, redness, crusting, and ulceration; these symptoms can sometimes mimic benign dermatologic conditions such as nipple eczema, making early diagnosis challenging. A 56-year-old woman presented with persistent erythema and scaling of the left nipple, which did not respond to conventional dermatologic treatments: a high degree of suspicion prompted further investigation. Reflectance confocal microscopy (RCM) revealed atypical, enlarged epidermal cells with irregular boundaries, while line-field confocal–optical coherence tomography (LC-OCT) demonstrated thickening of the epidermis, hypo-reflective vacuous spaces and abnormally large round cells (Paget cells). These non-invasive imaging findings were consistent with an aggressive case of Paget disease despite the absence of clear mammographic evidence of underlying carcinoma: in fact, several biopsies were needed, and at the end, massive surgery was necessary. Non-invasive imaging techniques, such as dermoscopy, RCM, and LC-OCT, offer a valuable diagnostic tool in detecting Paget disease, especially in early stages and atypical forms. Full article

Poultry feces, a critical biomarker for health assessment, requires timely and accurate pathological identification for food safety. Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. Key innovations include the following: (1) Integrating MASA(Manhattan self-attention)and DSconv (Depthwise Separable convolution) into the backbone network to mitigate feature confusion. (2) Utilizing a pre-trained BERT to extract textual semantic features, reducing annotation dependency and cost. (3) Designing a lightweight Gated Cross-Attention (GCA) module for dynamic multimodal fusion, achieving a 41% parameter reduction versus cross-modal transformers. Experiments demonstrate that MMCD significantly outperforms single-modal baselines in Accuracy (+8.69%), Recall (+8.72%), Precision (+8.67%), and F1 score (+8.72%). It surpasses simple feature concatenation by 2.51–2.82% and reduces parameters by 7.5M and computations by 1.62 GFLOPs versus the base ResNet50. This work validates multimodal fusion’s efficacy in pathological fecal detection, providing a theoretical and technical foundation for agricultural health monitoring systems. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal malignancies, with a five-year survival rate below 12%, largely attributable to its asymptomatic onset, late-stage diagnosis, and limited curative treatment options. Although PDAC accounts for approximately 3% of all cancers, it is projected to become the second leading cause of cancer-related mortality in the United States by 2030. A major contributor to its dismal prognosis is the lack of validated early detection strategies for asymptomatic individuals. In this review, we present a comprehensive synthesis of current advances in the early detection of PDAC, with a focus on the identification of high-risk populations, novel biomarker platforms, advanced imaging modalities, and artificial intelligence (AI)-driven tools. We highlight high-risk groups—such as those with new-onset diabetes after age 50, pancreatic steatosis, chronic pancreatitis, cystic precursor lesions, and hereditary cancer syndromes—as priority populations for targeted surveillance. Novel biomarker panels, including circulating tumor DNA (ctDNA), miRNAs, and exosomes, have demonstrated improved diagnostic accuracy in early-stage disease. Recent developments in imaging, such as multiparametric MRI, contrast-enhanced endoscopic ultrasound, and molecular imaging, offer improved sensitivity in detecting small or precursor lesions. AI-enhanced radiomics and machine learning models applied to prediagnostic CT scans and electronic health records are emerging as valuable tools for risk prediction prior to clinical presentation. We further refine the Define–Enrich–Find (DEF) framework to propose a clinically actionable strategy that integrates these innovations. Collectively, these advances pave the way for personalized, multimodal surveillance strategies with the potential to improve outcomes in this historically challenging malignancy. Full article

As critical interfaces bridging biological systems and electronic devices, the performance of bioelectrodes directly determines the sensitivity, selectivity, and reliability of biosensors. Recent advancements in functional nanomaterials (e.g., carbon nanomaterials, metallic nanoparticles, 2D materials) have substantially enhanced the application potential of bioelectrodes in disease detection, metabolic monitoring, and early diagnosis through strategic material selection, structural engineering, interface modification, and antifouling treatment. This review systematically examines the latest progress in nanomaterial-enabled interface design of bioelectrodes, with particular emphasis on performance enhancements in electrophysiological/electrochemical signal acquisition and multimodal sensing technologies. We comprehensively analyze cutting-edge developments in dynamic metabolic parameter monitoring for chronic disease management, as well as emerging research on flexible, high-sensitivity electrode interfaces for early disease diagnosis. Furthermore, this work focused on persistent technical challenges regarding nanomaterial biocompatibility and long-term operational stability while providing forward-looking perspectives on their translational applications in wearable medical devices and personalized health management systems. The proposed framework offers actionable guidance for researchers in this interdisciplinary field. Full article

Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Objectives: This study aims to describe the clinical features and genetic findings of nine Vietnamese patients with autosomal recessive bestrophinopathy. Methods: This retrospective and cross-sectional study included individuals diagnosed with autosomal recessive bestrophinopathy at the Eye Clinic, Vietnam National Geriatric Hospital between May 2024 and April 2025. The patients underwent a visual acuity assessment, retinal multimodal imaging, and molecular testing through BEST1 gene sequencing. Results: Nine patients from seven unrelated families were included. The mean age was 38.6 years (range: 14.1–79.6). Visual acuity ranged from 20/20 to 20/125. All patients showed vitelliform lesions, subretinal deposits, and both intraretinal and subretinal fluid. Other main features included diffuse macular hyperfluorescence and hyperopia. Less common clinical features encompassed glaucoma, retinoschisis, outer retinal thinning, serous retinal detachment, retinal thickening, and thinning of the retinal pigment epithelium. Compound heterozygous or homozygous variants were detected in all patients. Among the five identified BEST1 variants, the most frequent were p.(A195V) and p.(R200*). One novel variant, p.(K289*), was detected. Conclusions: The main clinical retinal features of nine Vietnamese patients with autosomal recessive bestrophinopathy included vitelliform lesions, subretinal deposits, retinal fluid, and diffuse macular hyperfluorescence. The most common variants were p.(A195V) and p.(R200*). Additionally, the identification of various compound heterozygotes and a novel BEST1 variant expands the mutation spectrum of the disease. Full article

Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

This study presents a hybrid ensemble learning framework for the joint detection and motor severity prediction of Parkinson’s disease (PD) using biomedical voice features. The proposed architecture integrates a deep multimodal fusion model with dense expert pathways, multi-head self-attention, and multitask output branches to simultaneously perform binary classification and regression. To ensure data quality and improve model generalization, preprocessing steps included outlier removal via Isolation Forest, two-stage feature scaling (RobustScaler followed by MinMaxScaler), and augmentation through polynomial and interaction terms. Borderline-SMOTE was employed to address class imbalance in the classification task. To enhance prediction performance, ensemble learning strategies were applied by stacking outputs from the fusion model with tree-based regressors (Random Forest, Gradient Boosting, and XGBoost), using diverse meta-learners including XGBoost, Ridge Regression, and a deep neural network. Among these, the Stacking Ensemble with XGBoost (SE-XGB) achieved the best results, with an R² of 99.78% and RMSE of 0.3802 for UPDRS regression and 99.37% accuracy for PD classification. Comparative analysis with recent literature highlights the superior performance of our framework, particularly in regression settings. These findings demonstrate the effectiveness of combining advanced feature engineering, deep learning, and ensemble meta-modeling for building accurate and generalizable models in voice-based PD monitoring. This work provides a scalable foundation for future clinical decision support systems. Full article

Background: Carnosine is a multimodal pleotropic endogenous molecule that exhibits properties that make it a compelling therapeutic agent for further evaluation in a number of diseases. However, little data currently exists on its pharmacokinetic profile, maximum tolerated doses, side effects and whether oral administration can lead to elevated brain concentrations. Method: To investigate this, sixteen healthy volunteers underwent a single dose-escalation study of oral carnosine to establish safety, tolerability, and pharmacokinetics. A subset (n = 5) underwent Proton Magnetic Resonance Imaging (MRI) spectroscopy to evaluate the effect of oral dosing on brain carnosine concentrations, and another subset (n = 4) completed a long-term (4-week) dosing study. Results: Oral carnosine was safe and well tolerated up to a dose of 10 g. At doses of 15 g, the frequency of adverse events became unacceptably high, with 77% of participants experiencing side effects, most commonly headache (43.5%), nausea (21.7%) and paraesthesia (21.7%). While pharmacokinetic profiles varied between individuals, peak plasma concentrations occurred within the first hour of dosing. Little circulating carnosine was detectable beyond 4 h. Brain carnosine concentration increased at 1 h post-dose but reverted to baseline values by 5 h. Long-term dosing at 5 g twice daily did not result in any adverse events. Conclusions: Our data will inform dosing interventions in future clinical trials of this exciting agent. Full article