Search Results (1,587)

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

Cardiac magnetic resonance (CMR) is a non-invasive imaging technique that plays a crucial role in the diagnosis, risk stratification, and management of a broad spectrum of cardiovascular diseases. Its high spatial resolution and ability to provide multiparametric tissue characterization make it uniquely suited for evaluating both structural and functional cardiac abnormalities. This review provides a comprehensive and clinically oriented overview of CMR applications in adult cardiology, structured into six main areas: (1) myocardial scarring in ischemic and non-ischemic cardiomyopathies, (2) infiltrative myocardial diseases, (3) adult congenital heart disease, (4) valvular heart disease, (5) pulmonary hypertension and right ventricular morpho-functional evaluation, and (6) cardio-oncology. In addition, technical considerations are also discussed. Finally, recommendations from recent guidelines issued by main international societies are integrated to support clinical decision-making. Full article

Prostatic artery embolization (PAE) is increasingly used as a primary minimally invasive treatment modality for lower urinary tract symptoms associated with benign prostatic hyperplasia. As a complex, fluoroscopic-guided endovascular procedure, PAE necessitates a significant use of ionizing radiation, raising important safety considerations for both patients and medical personnel. The objective of this review is to first summarize the procedural and anatomic fundamentals of PAE, and then to provide a comprehensive overview of the current literature on radiation dosimetry, establish contemporary benchmarks for dose metrics, and present an evidence-based guide to practical dose optimization strategies. Through a thorough review of published clinical studies, this article synthesizes reported values for key radiation indices, including Dose Area Product (DAP), Cumulative Air Kerma (CAK), and Fluoroscopy Time (FT). Furthermore, we will critically examine factors influencing dose variability—including patient complexity, procedural technique, and imaging technology—and will provide a practical, clinically oriented guide to implementing dose-saving measures. Ultimately, this review concludes that while PAE involves a non-trivial radiation burden, a thorough understanding and application of optimization principles can ensure the procedure is performed safely, reinforcing its role as a valuable therapy for BPH. Full article

Thin gold leaves were frequently used to embellish post-Byzantine religious panel paintings. Measuring their thickness using non-destructive methods is essential for understanding the technology behind their creation and developing effective preservation strategies. This study describes a method for non-invasively measuring the thickness of these gildings using large-scale imaging micro-X-ray fluorescence (µ-XRF) spectroscopy. The method relates the intensity of the Au Lα X-ray characteristic transition to the thickness of the gold layer. The method offers precise measurements of gold layer thickness in the submicrometer range on gilded surfaces, while traditional methods based on the intensity ratio of the same element prove ineffective. The method was initially validated on a mock-up sample created using traditional gilding techniques. Subsequently, the gilding was examined on two case studies of Greek religious icons. The analysis accurately measured the thickness of individual gold leaves, approximately one hundred nanometers, and identified regions with multiple overlapping layers, corresponding to structures with up to four leaves. The findings confirm that this technique offers valuable quantitative insights into the materiality and artistic techniques of these cultural heritage artifacts. Full article

Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation. Full article

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling—by capturing the metabolic phenotype of tumors—has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Background/Objectives: High-frequency ultrasonography (HFUS) has gained increasing attention in dermatology as a non-invasive imaging technique capable of visualizing cutaneous structures with high resolution. In cutaneous T-cell lymphomas (CTCL), including mycosis fungoides (MF)/Sézary syndrome (SS), HFUS may provide an objective method for assessing disease activity and monitoring treatment response. This study aimed to evaluate the clinical utility of HFUS in detecting therapy-induced changes in subepidermal low-echogenic band (SLEB) thickness. Methods: We conducted a prospective, single-center study between May 2021 and May 2025. Thirty-three patients with histologically confirmed MF (n = 31) or SS (n = 2) underwent HFUS at baseline and after 4–8 weeks of treatment. SLEB thickness was measured before (E1) and after early treatment (E2). Patients received systemic agents, phototherapy, or topical regimens. Statistical analysis included mixed-model ANOVA with repeated measures to assess SLEB changes, and post hoc tests were applied to explore the influence of therapy type, age, and gender. Results: Among 31 evaluable patients with MF, HFUS revealed a significant reduction in SLEB thickness after treatment (0.90 ± 1.10 mm vs. 0.69 ± 0.89 mm; F(1,29) = 8.88, p = 0.006, η² = 0.23). The type of early therapy (systemic vs. topical) did not significantly affect outcomes (p = 0.452). Age emerged as a relevant factor: patients ≥ 66 years exhibited higher baseline SLEB values and a significant reduction post-treatment (p < 0.001), whereas no comparable effect was observed in younger patients. Gender did not significantly influence SLEB changes. Conclusions: HFUS is a sensitive and clinically applicable imaging tool for monitoring treatment response in MF/SS. Reductions in SLEB thickness were observed across therapeutic modalities and aligned with early clinical improvement. HFUS may serve as a valuable adjunct to standard clinical and histopathological evaluation in the routine management of MF/SS. Full article

Background: Deep infiltrating endometriosis (DIE) frequently affects the posterior pelvic compartment, where accurate non-invasive imaging is essential for diagnosis and surgical planning. Aim: This systematic review evaluates the diagnostic performance of transvaginal ultrasound (TVUS) in detecting posterior compartment DIE, specifically rectosigmoid lesions, uterosacral ligament involvement, and pouch of Douglas obliteration. Material and Methods: A comprehensive literature search of PubMed, Scopus, and Web of Science was performed for studies published between 2015 and 2025. Eligible studies assessed the accuracy of TVUS for posterior compartment DIE using laparoscopy and histology as reference standards. Data on sensitivity, specificity, and overall diagnostic accuracy were extracted or derived. The study’s quality was evaluated using the QUADAS-2 tool. Results: Thirty eligible studies were included. The mean sensitivities and specificities reported in the included studies reached 83.05% and 90.53% for rectosigmoid disease, 78.07% and 90.49% for uterosacral ligament involvement, and 79.58% and 89.75% for pouch of Douglas obliteration, respectively. Adjunctive techniques such as gel sonovaginography, rectal water contrast, or saline instillation into the pouch of Douglas were described, but their use was inconsistent. Marked heterogeneity in patient preparation, scanning protocols, and reporting limited comparability across studies. Despite this, TVUS demonstrated diagnostic performance within a similar range to that reported for MRI in prior systematic reviews, with the advantages of lower cost, accessibility, and integration into routine gynecological practice. Conclusions: TVUS is consistently reported as a reliable and cost-effective imaging modality and, in line with international guidelines, should be considered the first-line option for posterior compartment DIE, though further standardization of scanning and reporting protocols is needed to optimize reproducibility and clinical utility. Full article

Background/Objectives: The glymphatic system is a recently characterized glial-dependent waste clearance pathway in the brain, which makes use of perivascular spaces for cerebrospinal fluid exchange. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) offers a non-invasive method for estimating perivascular flow, but its biological specificity and susceptibility to methodological variation, particularly head position during MRI acquisition, remain as threats to the validity of this technique. This study aimed to assess the prevalence of current DTI-ALPS practices, evaluate the impact of head orientation on ALPS index calculation, and propose a novel computational approach to improve measurement validity. Methods: We briefly reviewed DTI-ALPS literature to determine the use of head-orientation correction strategies. We then analyzed diffusion MRI data from 172 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to quantify the influence of head orientation on ALPS indices computed using the conventional Unrotated-ALPS, a vecrec-corrected ALPS, and the new LD-ALPS method proposed within. Results: A majority of studies employed Unrotated-ALPS, which does not correct for head orientation. In our sample, Unrotated-ALPS values were significantly associated with absolute head pitch (r₁₆₉ = −0.513, p < 0.001), indicating systematic bias. This relationship was eliminated using either vecreg or LD-ALPS. Additionally, LD-ALPS showed more sensitivity to cognitive status as measured by Mini-Mental State Examination scores. Conclusions: Correcting for head orientation is essential in DTI-ALPS studies. The LD-ALPS method, while computationally more demanding, improves the reliability and sensitivity of perivascular fluid estimates, supporting its use in future research on aging and neurodegeneration. Full article

Arrhythmogenic cardiomyopathy (ACM; MIM #107970) is a primitive heart muscle disease characterized by progressive myocardial loss and fibrosis or fibrofatty replacement, predisposing patients to ventricular arrhythmias, sudden cardiac death, and heart failure. Despite advances in imaging and genetics, early diagnosis remains challenging due to incomplete penetrance, variable phenotypic expressivity, and the fact that fatal arrhythmic events may often occur in the early stages of the disease. In this context, the identification of reliable biomarkers could enhance diagnostic accuracy, support risk stratification, and guide clinical management. This narrative review examines the current landscape of potential and emerging biomarkers in ACM, including troponins, natriuretic peptides, inflammatory proteins, microRNAs, fibrosis-related markers, and other molecules. Several of these biomarkers have demonstrated associations with disease severity, arrhythmic burden, or structural progression, although their routine clinical utility remains limited. The increasing relevance of genetic testing and non-invasive tissue characterization—particularly through cardiac imaging techniques—should also be emphasized as part of a multimodal diagnostic strategy in which biomarkers may play a complementary role. Although no single biomarker currently meets the criteria for a standalone diagnostic application, ongoing research into multi-marker panels and novel molecular targets offers promising perspectives. In conclusion, the integration of circulating biomarkers with imaging findings, genetic data, and clinical parameters may open new avenues for improving early detection and supporting personalized therapeutic strategies in patients with suspected ACM. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Flexible laryngoscopy (FL) is the standard diagnostic tool for vocal cord paralysis (VCP), but it involves patient discomfort, and its interpretation is subjective and operator-dependent. Dynamic digital radiography (DDR) is a novel imaging technique that acquires high-resolution sequential radiographs at a low radiation dose. While DDR has been widely applied in chest and diaphragmatic imaging, its use for laryngeal motion analysis has been poorly investigated. We present the case of a 50-year-old male referred for Computed Tomography (CT) of the neck and chest for suspected vocal cord paralysis. The referring physician did not specify the side of the suspected paralysis. Due to a language barrier and the absence of prior documentation, a detailed history could not be obtained. To assess vocal cord motion, we performed, for the first time in our Institution, a DDR study of the neck. During phonation maneuvers, DDR demonstrated fixation of the left vocal cord in an adducted paramedian position. CT confirmed this finding and did not highlight any further anomaly. This case demonstrates the feasibility of DDR as a low-cost, low-dose, non-invasive technique for functional evaluation of the larynx and may represent a valuable complementary imaging tool in laryngeal functional assessment. Full article

Sarcopenia, a progressive age-related loss of skeletal muscle mass, strength, and function, is a major contributor to disability, reduced quality of life, and mortality in older adults. While current diagnostic approaches, such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), magnetic resonance imaging (MRI), and computed tomography (CT), are widely used to assess muscle mass, they have limitations in detecting early qualitative changes in muscle architecture and composition. Shear Wave Elastography (SWE), an ultrasound-based technique that quantifies tissue stiffness, has emerged as a promising tool to evaluate both muscle quantity and quality in a non-invasive, portable, and reproducible manner. Studies suggest that SWE can detect alterations in muscle mechanical properties associated with sarcopenia, providing complementary information to traditional morphometric assessments. Preliminary evidence indicates its good reproducibility, feasibility in various clinical settings, and potential for integration into routine evaluations. This narrative review summarizes current evidence on the use of SWE for the assessment of sarcopenia across diverse populations. Full article

This study investigates the comparative performance of foundational models, advanced large-kernel architectures, and traditional deep learning approaches for hyperpolarized gas MRI segmentation across progressive data reduction scenarios. Chronic obstructive pulmonary disease (COPD) remains a leading global health concern, and advanced imaging techniques are crucial for its diagnosis and management. Hyperpolarized gas MRI, utilizing helium-3 (³He) and xenon-129 (¹²⁹Xe), offers a non-invasive way to assess lung function. We evaluated foundational models (Segment Anything Model and MedSAM), advanced architectures (UniRepLKNet and TransXNet), and traditional deep learning models (UNet with VGG19 backbone, Feature Pyramid Network with MIT-B5 backbone, and DeepLabV3 with ResNet152 backbone) using four data availability scenarios: 100%, 50%, 25%, and 10% of the full training dataset (1640 2D MRI slices from 205 participants). The results demonstrate that foundational and advanced models achieve statistically equivalent performance across all data scenarios (p > 0.01), while both significantly outperform traditional architectures under data constraints (p < 0.001). Under extreme data scarcity (10% training data), foundational and advanced models maintained DSC values above 0.86, while traditional models experienced catastrophic performance collapse. This work highlights the critical advantage of architectures with large effective receptive fields in medical imaging applications where data collection is challenging, demonstrating their potential to democratize advanced medical imaging analysis in resource-limited settings. Full article