Search Results (1,608)

Artificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on unseen images, making them challenging to provide as a service. One of the primary reasons for the lack of robustness is overfitting, resulting from errors and inconsistencies in the ground truth (GT). In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. Because of the variations in sample exposure during training, these models yield slightly different inference results. This variability allows for the estimation of pixel-level confidence levels for the GT. The confidence level is incorporated into the GT to influence the loss calculation during the training of the enhanced model. Furthermore, we implemented a consensus model that employs modified masks, where classes with low confidence are substituted by the dominant classes identified through a majority vote from the redundant models. To further improve robustness, we extended the same approach to fuse the dataset with different class compositions based on imagery from the Korea Multipurpose Satellite 3A (KOMPSAT-3A). Performance evaluations were conducted on three network architectures: a simple network, U-Net, and DeepLabV3. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network architectures. In the multi-dataset scenario, the enhanced models and consensus models showed an average performance improvement of 3.37% and 3.02% across the network architectures, respectively, compared to an average increase of 1.55% without the proposed method. Full article

Background and Objectives: this pilot study aimed to evaluate the diagnostic accuracy of ChatGPT-4o in analyzing oral mucosal lesions from clinical images. Materials and Methods: a total of 110 clinical images, including 100 pathological lesions and 10 healthy mucosal images, were retrieved from Google Images and analyzed by ChatGPT-4o using a standardized prompt. An expert panel of five clinicians established a reference diagnosis, categorizing lesions as benign or malignant. The AI-generated diagnoses were classified as correct or incorrect and further categorized as plausible or not plausible. The accuracy, sensitivity, specificity, and agreement with the expert panel were analyzed. The Artificial Intelligence Performance Instrument (AIPI) was used to assess the quality of AI-generated recommendations. Results: ChatGPT-4o correctly diagnosed 85% of cases. Among the 15 incorrect diagnoses, 10 were deemed plausible by the expert panel. The AI misclassified three malignant lesions as benign but did not categorize any benign lesions as malignant. Sensitivity and specificity were 91.7% and 100%, respectively. The AIPI score averaged 17.6 ± 1.73, indicating strong diagnostic reasoning. The McNemar test showed no significant differences between AI and expert diagnoses (p = 0.084). Conclusions: In this proof-of-concept pilot study, ChatGPT-4o demonstrated high diagnostic accuracy and strong descriptive capabilities in oral mucosal lesion analysis. A residual 8.3% false-negative rate for malignant lesions underscores the need for specialist oversight; however, the model shows promise as an AI-powered triage aid in settings with limited access to specialized care. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Background/Objectives: The inferior thyroid artery (ITA) is an essential component of the thyroid gland’s vasculature, with significant clinical and surgical implications due to its anatomical variability. This systematic review aimed to describe the prevalence of ITA anatomical variants and their association with clinical conditions or surgical implications. Methods: A comprehensive search was conducted in MEDLINE, Web of Science, Google Scholar, CINAHL, Scopus, and EMBASE on 20 November 2025. Eligibility criteria included studies reporting on the presence of ITA variants and their correlation with pathologies. Two authors independently screened the literature, extracted data, and assessed methodological quality using the AQUA and JBI tools. Results: Of the 2647 articles identified, 19 studies involving 1118 subjects/cadavers were included. Variations in ITA origin, absence, and additional arteries were reported, with the most common variant being direct origin from the subclavian artery. Clinically, these variations were associated with increased risk of intraoperative hemorrhage, potential nerve damage, and challenges in preoperative planning, particularly during thyroidectomy and other neck procedures. Conclusions: Understanding the anatomical diversity of the ITA is crucial for reducing surgical risks and improving patient outcomes. The review highlighted the need for more standardized research protocols and comprehensive data reporting to enhance the quality of evidence in this domain. Preoperative imaging and thorough anatomical assessments tailored to individual patient profiles, considering ethnic and gender-related differences, are essential for safe surgical interventions in the thyroid region. Full article

Background: Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease and a major cause of morbidity and mortality among preterm infants. Traditional diagnostic methods such as abdominal radiography have limited sensitivity in early disease stages, prompting interest in bowel ultrasound (BUS) as a complementary imaging modality. Objective: This scoping review aims to synthesize existing literature on the role of ultra sound in the early detection, diagnosis, and management of NEC, with emphasis on its diagnostic performance, integration into clinical care, and technological innovations. Methods: Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Embase, Cochrane Library, and Google Scholar for studies published between January 2000 and December 2025. Inclusion criteria encompassed original research, reviews, and clinical studies evaluating the use of bowel, intestinal, or Doppler ultrasound in neonates with suspected or confirmed NEC. Data were extracted, categorized by study design, population characteristics, ultrasound features, and diagnostic outcomes, and qualitatively synthesized. Results: A total of 101 studies were included. BUS demonstrated superior sensitivity over radiography in detecting early features of NEC, including bowel wall thickening, portal venous gas, and altered peristalsis. Doppler ultrasound, both antenatal and postnatal, was effective in identifying perfusion deficits predictive of NEC onset. Neonatologist-performed ultrasound (NEOBUS) showed high interobserver agreement when standardized protocols were used. Emerging tools such as ultra-high-frequency ultrasound (UHFUS) and artificial intelligence (AI)-enhanced analysis hold potential to improve diagnostic precision. Point-of-care ultrasound (POCUS) appears feasible in resource-limited settings, though implementation barriers remain. Conclusions: Bowel ultrasound is a valuable adjunct to conventional imaging in NEC diagnosis. Standardized protocols, validation of advanced technologies, and out come-based studies are essential to guide its broader clinical adoption. Full article

Purpose: Pellucid Marginal Degeneration (PMD) is a rare ectatic corneal disorder characterized by inferior peripheral thinning and significant irregular astigmatism. Despite its clinical similarities to keratoconus, PMD presents unique diagnostic and therapeutic challenges. This review aims to provide a comprehensive update on the pathophysiology, clinical features, diagnostic approaches, and management strategies for PMD, emphasizing the latest advancements in treatment options. Methods: A systematic literature search was performed in MEDLINE (via PubMed), Google Scholar, and Scopus up to February 2025 using the terms: “pellucid marginal degeneration,” “PMD,” “ectatic corneal disorders,” “keratoplasty in PMD,” “corneal cross-linking in PMD,” “ICRS in PMD,” “toric IOL PMD” and their Boolean combinations (AND/OR). The search was restricted to English-language studies involving human subjects, including case reports, case series, retrospective studies, clinical trials, and systematic reviews. A total of 76 studies met the inclusion criteria addressing treatment outcomes in PMD. Results: PMD is characterized by a crescent-shaped band of inferior corneal thinning, leading to high irregular astigmatism and reduced visual acuity. Diagnosis relies on advanced imaging techniques such as Scheimpflug-based corneal tomography, which reveals the characteristic “crab-claw” pattern. Conservative management includes rigid gas-permeable (RGP) lenses and scleral lenses, which provide effective visual rehabilitation in mild to moderate cases. Surgical options, such as CXL, ICRS, and toric IOLs, are reserved for advanced cases, with varying degrees of success. Newer techniques such as CAIRS, employing donor tissue instead of synthetic rings, show promising outcomes in corneal remodeling with potentially improved biocompatibility. Penetrating keratoplasty (PK) and deep anterior lamellar keratoplasty (DALK) remain definitive treatments for severe PMD, though they are associated with significant risks, including graft rejection and postoperative astigmatism. Conclusions: PMD is a complex and progressive corneal disorder that requires a tailored approach to management. Early diagnosis and intervention are critical to optimizing visual outcomes. While conservative measures are effective in mild cases, surgical interventions offer promising results for advanced disease. Further research is needed to refine treatment protocols and improve long-term outcomes for patients with PMD. Full article

Remote sensing has emerged as a promising technology for large-scale detection and updating of global wind turbine databases. High-resolution imagery (e.g., Google Earth) facilitates the identification of offshore wind turbines (OWTs) but offers limited offshore coverage due to the high cost of capturing vast ocean areas. In contrast, medium-resolution imagery, such as 10-m Sentinel-2, provides broad ocean coverage but depicts turbines only as small bright spots and shadows, making accurate detection challenging. To address these limitations, We propose a novel deep learning approach to capture the variability in OWT appearance and shadows caused by changes in solar illumination and satellite viewing geometry. Our method learns intrinsic, imaging geometry-invariant features of OWTs, enabling robust detection across multi-seasonal Sentinel-2 imagery. This approach is implemented using Faster R-CNN as the baseline, with three enhanced extensions: (1) direct integration of imaging parameters, where Geowise-Net incorporates solar and view angular information of satellite metadata to improve geometric awareness; (2) implicit geometry learning, where Contrast-Net employs contrastive learning on seasonal image pairs to capture variability in turbine appearance and shadows caused by changes in solar and viewing geometry; and (3) a Composite model that integrates the above two geometry-aware models to utilize their complementary strengths. All four models were evaluated using Sentinel-2 imagery from offshore regions in China. The ablation experiments showed a progressive improvement in detection performance in the following order: Faster R-CNN < Geowise-Net < Contrast-Net < Composite. Seasonal tests demonstrated that the proposed models maintained high performance on summer images against the baseline, where turbine shadows are significantly shorter than in winter scenes. The Composite model, in particular, showed only a 0.8% difference in the F1 score between the two seasons, compared to up to 3.7% for the baseline, indicating strong robustness to seasonal variation. By applying our approach to 887 Sentinel-2 scenes from China’s offshore regions (2023.1–2025.3), we built the China OWT Dataset, mapping 7369 turbines as of March 2025. Full article

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

Background: This systematic review aimed to comprehensively evaluate the clinical, diagnostic, and therapeutic features of synchronous acute cholecystitis (AC) and acute appendicitis (AAP). Methods: The review protocol was prospectively registered in PROSPERO (CRD420251086131) and conducted in accordance with PRISMA 2020 guidelines. A systematic search was performed across PubMed, MEDLINE, Web of Science, Scopus, Google Scholar, and Google databases for studies published from January 1975 to May 2025. Search terms included variations of “synchronous,” “simultaneous,” “concurrent,” and “coexistence” combined with “appendicitis,” “appendectomy,” “cholecystitis,” and “cholecystectomy.” Reference lists of included studies were screened. Studies reporting human cases with sufficient patient-level clinical data were included. Data extraction and quality assessment were performed independently by pairs of reviewers, with discrepancies resolved through consensus. No meta-analysis was conducted due to the descriptive nature of the data. Results: A total of 44 articles were included in this review. Of these, thirty-four were available in full text, one was accessible only as an abstract, and one was a literature review, while eight articles were inaccessible. Clinical data from forty patients, including two from our own cases, were evaluated, with a median age of 41 years. The gender distribution was equal, with a median age of 50 years among male patients and 36 years among female patients. Leukocytosis was observed in 25 of 33 patients with available laboratory data. Among 37 patients with documented diagnostic methods, ultrasonography and computed tomography were the most frequently utilized modalities, followed by physical examination. Twenty-seven patients underwent laparoscopic cholecystectomy and appendectomy. The remaining patients were managed with open surgery or conservative treatment. Postoperative complications occurred in five patients, including sepsis, perforation, leakage, diarrhea, and wound infections. Histopathological analysis revealed AAP in 25 cases and AC in 14. Additional findings included gangrenous inflammation and neoplastic lesions. Conclusions: Synchronous AC and AAP are rare and diagnostically challenging conditions. Early recognition via imaging and clinical evaluation is critical. Laparoscopic management remains the preferred approach. Histopathological examination of surgical specimens is essential for identifying unexpected pathology, thereby guiding appropriate patient management. Full article

This study employs a deep learning-based object detection model, GoogleNet, to identify cracks in cultural heritage images. Subsequently, a semantic segmentation model, SegNet, is utilized to determine the location and extent of the cracks. To establish a scale ratio between image pixels and real-world dimensions, a parallel laser-based measurement approach is applied, enabling precise crack length calculations. The results indicate that the percentage error between crack lengths estimated using deep learning and those measured with a caliper is approximately 3%, demonstrating the feasibility and reliability of the proposed method. Additionally, the study examines the impact of iteration count, image quantity, and image category on the performance of GoogleNet and SegNet. While increasing the number of iterations significantly improves the models’ learning performance in the early stages, excessive iterations lead to overfitting. The optimal performance for GoogleNet was achieved at 75 iterations, whereas SegNet reached its best performance after 45,000 iterations. Similarly, while expanding the training dataset enhances model generalization, an excessive number of images may also contribute to overfitting. GoogleNet exhibited optimal performance with a training set of 66 images, while SegNet achieved the best segmentation accuracy when trained with 300 images. Furthermore, the study investigates the effect of different crack image categories by classifying datasets into four groups: general cracks, plain wall cracks, mottled wall cracks, and brick wall cracks. The findings reveal that training GoogleNet and SegNet with general crack images yielded the highest model performance, whereas training with a single crack category substantially reduced generalization capability. Full article

Morphological problems for distinguishing between glacier ice, glacier ice with a debris cover (debris-covered glaciers), and rock glaciers are outlined with respect to recognising and mapping these features. Decimal latitude–longitude [dLL] values are used for geolocation. One model for rock glacier formation and flow discusses the idea that they consist of ‘mountain permafrost’. However, signs of permafrost-derived ice, such as flow features, have not been identified in these landsystems; talus slopes in the neighbourhoods of glaciers and rock glaciers. An alternative view, whereby rock glaciers are derived from glacier ice rather than permafrost, is demonstrated with examples from various locations in the mountain domain, 𝔻𝕞. A Google Earth and field examination of many rock glaciers shows glacier ice exposed below a rock debris mantle. Ice exposure sites provide ground truth for observations and interpretations stating that rock glaciers are indeed formed from glacier ice. Exposure sites include bare ice at the headwalls of cirques and above debris-covered glaciers; additionally, ice cliffs on the sides of meltwater pools are visible at various locations along the lengths of rock glaciers. Inspection using Google Earth shows that these pools can be traced downslope and their sizes can be monitored between images. Meltwater pools occur in rock glaciers that have been previously identified in inventories as being indictive of permafrost in the mountain domain. Glaciers with a thick rock debris cover exhibit ‘hidden ice’ and are shown to be geomorphological units mapped as rock glaciers. Full article

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. Full article

Background: Tooth agenesis, particularly hypodontia, poses a clinical and esthetic challenge in growing patients due to limitations in definitive implant placement before skeletal maturity. Traditional solutions such as removable prostheses or orthodontic space closure often fail to provide adequate long-term stability, function, and tissue preservation. In recent years, orthodontic mini-implants have emerged as a promising interim solution. This narrative review aims to synthesize current clinical evidence on the use of orthodontic mini-implants as temporary prosthetic abutments in children and adolescents with hypodontia or post-traumatic tooth loss. Methods: A literature search was conducted using PubMed and Google Scholar databases, covering studies published between January 2004 and March 2025. Inclusion criteria were clinical reports involving skeletally immature patients with congenital or traumatic tooth loss treated with mini-implants, with mandatory radiographic diagnostics and outcome data. Data extracted included patient demographics, etiology, implant site, imaging, follow-up, complications, and outcomes. A total of 17 studies comprising 42 cases were analyzed and summarized in tabular form. Results: Patients aged 6 to 16 years were treated primarily for agenesis of maxillary lateral or central incisors. The mean follow-up duration was 36.9 months. CBCT was used in 28.6% of cases. Mini-implants demonstrated high clinical success with stable soft tissue contours and preservation of alveolar volume. Complications were reported in 21.4% of cases and included crown debonding, minor infraocclusion, soft tissue irritation, and rare instances of osseointegration. Conclusions: Orthodontic mini-implants may provide a minimally invasive and reversible approach to interim tooth replacement in growing patients. Preliminary evidence suggests favorable outcomes in terms of stability, esthetics, and tissue preservation, but further prospective research is needed to validate their long-term effectiveness and standardize clinical application. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

Under the background of global climate change, shallow landslide clusters induced by extreme rainfall are occurring with increasing frequency, causing severe casualties and economic losses. To address this challenge, this study proposes an integrated approach to support both emergency response and long-term mitigation for rainfall-induced shallow landslides. The workflow includes (1) rapid landslide detection based on time-series image fusion and threshold segmentation on the Google Earth Engine (GEE) platform; (2) numerical simulation of landslide runout using the R.avaflow model; (3) landslide susceptibility assessment based on event-driven inventories and machine learning; and (4) delineation of high-risk slopes by integrating simulation outputs, susceptibility results, and exposed elements. Applied to Qugaona Township in Zhouqu County, Bailong River Basin, the framework identified 747 landslides. The R.avaflow simulations captured the spatial extent and depositional features of landslides, assisting post-disaster operations. The Gradient Boosting-based susceptibility model achieved an accuracy of 0.870, with 8.0% of the area classified as highly susceptible. In Cangan Village, high-risk slopes were delineated, with 31.08%, 17.85%, and 22.42% of slopes potentially affecting buildings, farmland, and roads, respectively. The study recommends engineering interventions for these areas. Compared with traditional methods, this approach demonstrates greater applicability and provides a more comprehensive basis for managing rainfall-induced landslide hazards. Full article