Search Results (91)

Ovarian cancer (OC) is typically treated with cytoreductive surgery (CRS). Hyperspectral imaging (HSI) is an emerging non-invasive, label-free technique that enables whole-area scanning, making it a promising tool for real-time tumour recognition. However, developing tumour recognition algorithms requires a foundational understanding of spectral variability in normal tissues. This study focusses on the in vivo spectral profiles of key abdominal and pelvic organs encountered during CRS, including the uterus, ovaries, intestines, mesentery, omentum, peritoneum, and fallopian tubes, and evaluates the potential for organ recognition using HSI data. Intraoperative HSI data were from healthy patients. Two machine learning models, a support vector machine (SVM) and a 3D convolutional neural network (3DCNN), were trained to classify the organs based on their spectral signatures. In total, 15 patients were included in the dataset. The 3DCNN slightly outperformed the SVM in terms of the average accuracy (0.889 vs. 0.878), sensitivity (0.648 vs. 0.604), specificity (0.936 vs. 0.930), and Dice Similarity Coefficient (0.595 vs. 0.569). This study demonstrates the feasibility of using HSI for organ differentiation in the clinical setting, although in some cases separability remains a challenge, especially when organs have similar spectra. This is a critical step towards a generalizable in vivo abdominal tumour recognition algorithm, by carefully investigating spectral fingerprints of abdominal tissues. Full article

Open-set classification for remote sensing requires models that simultaneously achieve high accuracy on known land-cover types and reliably detect novel classes absent from the training distribution—a capability essential for real-world deployment where new classes routinely emerge. Existing multimodal fusion approaches for hyperspectral imagery (HSI) and LiDAR are primarily designed for closed-set scenarios and lack robust uncertainty modeling for unknown detection. We propose a post hoc calibrated multimodal open-set framework with three tightly integrated components. First, an Uncertainty-Aware Gating Fusion (UAGF) module dynamically weights HSI and LiDAR features per sample based on modality reliability and produces a gating uncertainty signal reflecting fusion confidence. Second, an Iterative Feedback Refinement (IFR) module progressively refines fused representations over multiple iterations and captures convergence dynamics, where stable convergence indicates known samples while high feature-change variance identifies potential unknowns. Third, a compact two-signal open-set detector combines gating uncertainty and refinement variance through an EVT (Weibull)-based post hoc calibration mechanism fitted exclusively on known validation samples. The framework follows a strict zero-unknown-supervision protocol: the multimodal backbone is trained using only known-class samples, and the open-set decision threshold is derived solely from the known validation score distribution. This design decouples representation of learning from open-set decision learning, improving robustness and avoiding the objective conflicts that arise in joint training. Comprehensive experiments on three benchmark datasets—Houston2013, Muufl, and Augsburg—demonstrate that the proposed method achieves 92.79%, 84.47%, and 80.99% overall accuracy and 76.48%, 63.91%, and 56.81% unknown accuracy, outperforming the closest multimodal competitor HyLiOSR by up to 32.4 pp in unknown accuracy while maintaining competitive closed-set performance. Full article

Hyperspectral image change detection (HSI-CD) is becoming increasingly important in understanding how the Earth’s surface evolves over time, from monitoring ecosystems to tracking urban expansion. Unlike traditional pixel-based or hand-crafted approaches, deep learning models can automatically learn powerful spectral–spatial features, making them especially effective for this task. In this review, we bring together recent advances in deep learning for HSI-CD, combining a meta-analysis of the literature with an overview of the main model families and training strategies. We cover supervised, semi-supervised, and unsupervised methods, as well as newer directions such as transfer learning, self-supervised frameworks, and hybrid designs that blend CNNs, transformers, and graph neural networks. We also discuss benchmark datasets, evaluation protocols, and case studies that show how these methods perform in practice. Beyond summarizing the current progress, the review highlights ongoing gaps, such as limited labeled data, generalization across sensors, computational efficiency, and the need for interpretability, and points to emerging opportunities for future work. Our goal is to provide both a snapshot of the current state of the field and a road map for advancing deep learning-based HSI-CD. Full article

Background and Objectives: Intraoperative assessment of tissue perfusion remains a decisive but imperfect step in abdominal surgery. Surgeons still rely heavily on visual judgement when choosing bowel transection lines, constructing anastomoses, judging intestinal viability, or assessing graft reperfusion, even though these decisions are directly linked to anastomotic leak, conduit ischemia, postoperative liver dysfunction, and graft failure. Hyperspectral imaging (HSI) is an emerging contrast-free optical technology that generates quantitative maps of tissue oxygenation, hemoglobin distribution, water content, and near-infrared perfusion. The present review was designed to evaluate whether clinical intraoperative HSI has matured sufficiently to support a focused systematic review topic in abdominal surgery and to synthesize the currently available human evidence. Methods: A literature search was conducted up to 20 February 2026 using combinations of the terms “hyperspectral imaging”, “HSI”, “abdominal surgery”, “colorectal”, “hepatectomy”, “transplantation”, “pancreatoduodenectomy”, “esophagectomy”, “mesenteric ischemia”, and “intraoperative”. Eligible records were original human clinical studies evaluating intraoperative HSI in abdominal or transplant-related operations with perfusion, oxygenation, or tissue viability as a central endpoint. Review articles, animal studies, non-surgical diagnostic studies, and single-patient case reports were excluded. Data were synthesized narratively because of major heterogeneity in indications, designs, devices, timing of measurements, and reported outcomes. Results: Thirteen studies published between 2019 and 2024 met the eligibility criteria, representing 391 patients. The literature covered colorectal resection, acute mesenteric ischemia, esophageal reconstruction with gastric or colonic conduits, pancreatoduodenectomy, pancreas transplantation, major hepatectomy, liver transplantation, and minimally invasive system validation. Across colorectal studies, HSI frequently demonstrated discordance between visually selected and objectively perfused transection lines, with clinically relevant strategy changes in a substantial proportion of patients. In ischemic and transplant settings, HSI discriminated poorly perfused tissue, identified low near-infrared perfusion values associated with early allograft dysfunction, and quantified reperfusion patterns after clamping or implantation. The evidence base was dominated by prospective single-center feasibility studies with small to moderate sample sizes, and no randomized trials were identified. Conclusions: Clinical intraoperative HSI in abdominal surgery is a genuinely niche yet rapidly expanding topic with a sufficient number of human studies to support a relevant systematic review. Current evidence consistently supports feasibility, quantitative perfusion discrimination, and plausible intraoperative utility, especially in colorectal and transplant-related surgery. However, the field remains methodologically heterogeneous, and the next research priority is multicenter standardization with clinically anchored thresholds and outcome-driven comparative studies. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Hyperspectral imaging (HSI) combined with chemometrics has emerged as a rapid and non-destructive technique for fruit quality evaluation, enabling efficient monitoring of biochemical changes during postharvest storage. Among quality indicators, antioxidant activity is closely associated with nutritional value and physiological stability. This study aimed to develop an HSI-based approach for assessing the antioxidant capacity of strawberries subjected to different pre-cooling treatments during storage. Strawberries were treated with five pre-cooling methods and stored for up to 41 days. Antioxidant activity was measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay. Hyperspectral data were collected and preprocessed using multiplicative scatter correction (MSC), followed by partial least squares regression (PLSR) to construct predictive models. Among the treatments, immersion vacuum cooling combined with one-cycle pulsing (IVCWP1) exhibited significantly higher DPPH scavenging activity (61.17 ± 12.31%) than immersion vacuum cooling with water (IVCW, 52.89 ± 18.30%) (p < 0.05). The PLSR model developed using MSC-corrected average reflectance spectra showed superior predictive performance and a higher coefficient of determination (R²) than models based on raw spectra. The results demonstrate that HSI coupled with chemometrics is an effective and practical tool for non-destructive evaluation of antioxidant activity and comparison of pre-cooling strategies in strawberries. Full article

Hyperspectral (HSI) remote sensing has emerged as a transformative technology for Earth Observation, enabling detailed assessments across different domains. The current PRISMA-aligned systematic review aims to compare classical physics-based algorithms with emerging machine learning (ML), deep learning (DL) and hybrid approaches across two relevant application domains (vegetation and raw materials), analyzing over 350 peer-reviewed studies (194 after the screening) sourced from Scopus and Web of Science and accessed in July 2025. Specific domain-related studies have been considered, excluding duplicates and studies not strictly related to HSI. Risk of bias was assessed qualitatively based on different criteria. The efficiency of the techniques was analyzed by comparing the accuracy metrics reported in the studies. The heterogeneity of the evaluation metrics used across the different categories of the studies and the underrepresentation of some application domains is the final baseline of the work. The results were synthesized, grouping by application domains and algorithm category: ML and DL models dominate vegetation applications, and physics-based methods remain prevalent in raw materials. Hybrid models achieve the highest performances across all domains. This review highlights the importance of the hyperspectral operational requirements identified for upcoming missions (CHIME, SBG and IRIDE) and points out the opportunity for algorithm development. Full article

This study investigates the challenge of affordable housing in Riyadh, a city undergoing rapid transformation aligned with Saudi Arabia’s Vision 2030. It aims to bridge the structural gap in the housing market by developing a comprehensive analytical framework that measures housing suitability for emerging middle-income families, linking it to economic, spatial, and behavioral dimensions. The research employs a sequential mixed-methods design. The first phase involved a Multi-Criteria Decision Analysis (MCDA) of 106 residential neighborhoods, constructing a Housing Suitability Index (HSI) based on financing cost (≤SAR 880,000), quality of urban life, and geographical accessibility. The second phase utilized focus groups with 16 participants from real estate developers and new families to explore behavioral drivers and subjective trade-offs. Quantitative results identified “convenience clusters” primarily in the city’s southeastern and southwestern sectors, offering an optimal balance between price and accessibility. Qualitative analysis revealed a significant trust gap and a misalignment of priorities: new families are increasingly willing to sacrifice unit size for central location and construction quality, a preference that conflicts with developers’ strategies focused on luxury units or peripheral projects for higher margins. The study concludes that achieving the 70% homeownership target requires a hybrid policy model, combining supply-side stimuli (e.g., subsidized land) with demand-side management (e.g., progressive mortgages). It recommends integrating the HSI into urban planning to direct investment towards logistically connected areas, fostering sustainable communities. Full article

We examine complex inequities that emerge when race, gender, and first-generation college status are treated as interdependent, rather than independent statuses, for assessing student outcomes at Hispanic-Serving Institutions (HSIs). Drawing on student-level administrative data from two public HSIs in the U.S. Southwest, we analyze four-year graduation and placement in developmental English and mathematics. Using continuing-generation college white women as the reference group, we estimate marginal effects and then construct linear combinations for twenty intersectional social locations defined by race, gender, and first-generation college status. Our findings show that first-generation American Indian men, first-generation college Black men, and first-generation college Hispanic men experience some of the largest achievement gaps in both graduation and developmental placement, gaps that would remain obscured in conventional reporting by race, gender, or class alone. We argue that quantitative intersectionality, grounded in critical race and intersectionality scholarship, offers a value-added approach to state-based institutional analytics that can inform equity metrics, accountability systems, and resource allocation at HSIs and beyond. We conclude with recommendations for redesigning data infrastructures, reporting practices, and equity initiatives to better align HSI servingness with the lived realities of structurally marginalized students. Full article

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN–HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN–HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors. Full article

Unmanned Aerial Spraying Systems (UASS) has rapidly advanced precision crop protection. However, the spray performance of UASSs is influenced by nozzle atomization, rotor-induced airflow, and external environmental conditions. These factors cause strong spatiotemporal coupling and high uncertainty. As a result, visualization-based monitoring techniques are now essential for understanding these dynamics and supporting spray modeling and drift-mitigation design. This review highlights developments in spray visualization technologies along the “droplet–airflow–target” chain mechanism in UASS spraying. We first outline the physical fundamentals of droplet formation, liquid-sheet breakup, droplet size distribution, and transport mechanisms in rotor-induced flow. Dominant processes are identified across near-field, mid-field, and far-field scales. Next, we summarize major visualization methods. These include optical imaging (PDPA/PDIA, HSI, DIH), laser-based scattering and ranging (LD, LiDAR), and flow-field visualization (PIV). We compare their spatial resolution, measurement range, 3D reconstruction capabilities, and possible sources of error. We then review wind-tunnel trials, field experiments, and point-cloud reconstruction studies. These studies show how downwash flow and tip vortices affect plume structure, canopy disturbance, and deposition patterns. Finally, we discuss emerging intelligent analysis for large-scale monitoring—such as image-based droplet recognition, multimodal data fusion, and data-driven modeling. We outline future directions, including unified feature systems, vortex-coupled models, and embedded closed-loop spray control. This review is a comprehensive reference for advancing UASS analysis, drift assessment, spray optimization, and smart support systems. Full article

Amid escalating attacks on the diversity, equity, and inclusion, Historically Black Emerging Hispanic-Serving Institutions (HBeHSIs) represent overlooked spaces of resistance in U.S. Higher education. This study examines how faculty and administrators negotiate racial and professional identities within institutions shaped by Black liberatory traditions and exclusionary HSI policy. Guided by Bradley and Tillis’s Afro-Latinidades heuristic, we link psychosocial identity development to institutional praxis and anti-colonial resistance. Interviews with 10 BIPOC professionals reveal identity ork as collective praxis challenging essentialist narratives and affirming servingness beyond enrollment metrics. Five themes illustrate work as collective praxis challenging essentialist narratives and affirming servingness beyond enrollment metrics. Five themes illustrate strategies for sustaining equity-driven missions under racial retrenchment, calling for renewed commitments to justice-centered higher education. Full article

Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient settings, and allows for the detection of tumor boundaries with over 90% accuracy, according to clinical studies. Originally developed for remote sensing and aerospace applications, HSI has rapidly evolved and found increasing relevance across diverse sectors, including agriculture, environmental monitoring, food safety, pharmaceuticals, defense, and especially medical diagnostics. This review explores the origins, development, and expanding applications of HSI, with a particular emphasis on its role in healthcare. It discusses the operational principles and unique features of hyperspectral systems, such as their ability to produce spectral data cubes, perform non-destructive analysis, and integrate with emerging technologies like artificial intelligence and drone-based platforms. By comparing hyperspectral imaging to traditional and multispectral techniques, the review highlights its superior spectral resolution and versatility. Key challenges, including data volume, sensor calibration, and real-time processing, are also addressed. Finally, emerging trends such as miniaturization, integration with the Internet of Things, and sustainable system designs are examined, offering insights into the future directions and interdisciplinary potentials of HSI in both scientific research and practical applications. Full article

Hamstring strain injuries (HSI) are common in soccer and remain challenging to predict, as traditional risk factors often fail to capture the multifactorial nature of injury susceptibility. This prospective cohort study aimed to develop and internally validate a machine learning-assisted logistic regression model for predicting hamstring injuries in amateur soccer players using preseason clinical and strength-related variables. A total of 120 male players were followed for one competitive season (30 weeks). Baseline predictors included age, body mass index, previous injury, and bilateral isometric hip and knee strength measured via handheld dynamometry. Twenty initial predictors were reduced to ten through symmetrical uncertainty feature ranking before training a logistic regression model with elastic-net regularization (training set: n = 83; test set: n = 37) using nested four-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration metrics, and confusion matrices. During follow-up, 21 players sustained at least one HSI (32 events; 28% reinjuries), yielding an events-per-variable ratio of 2.1, below ideal thresholds and suggesting possible overfitting. On the independent test set, the model achieved an accuracy of 64.9%, AUC of 0.68 (95% CI 0.52–0.84), calibration slope of 0.85, and intercept of −0.12, with a sensitivity of 60% and specificity of 65.6%. Dominant-leg hip abduction strength was the only statistically significant predictor (OR = 0.82, 95% CI 0.70–0.96), while permutation importance analyses identified previous hamstring injury as the most stable contributor to model performance. Neither age nor hamstring isometric strength demonstrated predictive value. Although model discrimination was moderate and calibration indicated mild overfitting, findings reinforce the prognostic relevance of prior injury and suggest that reduced hip abduction strength may serve as an emerging candidate marker. This study, classified as a TRIPOD Category 2 model (development without external validation), provides preliminary, hypothesis-generating evidence supporting the use of multivariate strength and history-based predictors in future, larger-scale injury prediction research. Full article

Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article