Search Results (3,343)

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

Background/Objectives: High-grade endometrial cancer (EC) is associated with poor outcomes, particularly in populations with a high burden of aggressive histologies. There is a critical need for accessible biomarkers to improve prognostic assessment and guide clinical management. Methods: In this study, we evaluated the feasibility and clinical relevance of monitoring circulating tumor DNA (ctDNA) by tracking somatic TP53 mutations using a routine next-generation sequencing (NGS) assay already implemented in diagnostic practice. Results: Among 21 patients with high-grade EC carrying TP53 mutations in the primary tumor, ctDNA was detectable in over 75% during follow-up. Baseline ctDNA detection strongly correlated with advanced disease: none of the FIGO I tumors were ctDNA-positive at diagnosis, whereas 73% of FIGO > I tumors showed detectable ctDNA. Patients with ctDNA detected at baseline had significantly poorer outcomes, with a 2-year recurrence-free survival (RFS) of 18% versus 60% and a 2-year overall survival (OS) of 40% versus 78%. Longitudinal monitoring revealed that postoperative persistence or reappearance of ctDNA was consistently associated with disease progression, often preceding radiological relapse. Conversely, early ctDNA clearance (at M4–M8) was associated with more favorable clinical trajectories. Conclusions: These findings highlight the potential role of ctDNA as a real-time molecular marker of minimal residual disease and tumor dynamics. Our results demonstrate that TP53-based ctDNA tracking using a standard NGS panel is feasible, sensitive, and clinically informative in high-grade EC. This approach may contribute to improving prognostic stratification and enabling more personalized, responsive clinical management, particularly in high-risk populations. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

Background/Objectives: Having longer mesocotyls is beneficial for the deep-sowing tolerance of rice, which is important for seedling establishment. Methods: Here, we performed transcriptome analysis of the elongating mesocotyl of Zhengdao 209 in response to three different sowing depths to identify the pivotal genes regulating rice mesocotyl elongation. Results: Three groups with different mesocotyl lengths were compared using transcriptome analysis, and 60 common differentially expressed genes were detected. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that these genes are primarily involved in phenylpropanoid biosynthesis, cutin suberine and wax biosynthesis, the plant mitogen-activated protein kinase signaling pathway, diterpenoid biosynthesis, cyanoamino acid metabolism, carbon fixation in photosynthetic organisms, flavonoid biosynthesis, and glutathione metabolism. Furthermore, weighted gene co-expression network and hierarchical clustering analyses showed that most of the differentially expressed genes are implicated in phenylpropanoid biosynthesis, carbon metabolism, photosynthesis antenna proteins, and plant–pathogen interactions. Among the genes involved in phenylpropanoid biosynthesis processes, the expression levels of OsPHT3 and LOC_Os04g59260 increased, while OsCCR1, OsPGIP4, and LOC_Os01g45110 expression decreased with increasing sowing depth. Among the genes involved in the mitogen-activated protein kinase signaling pathway, the expression levels of LOC_Os07g03319 and LOC_Os07g03580 increased, while LOC_Os07g03409 decreased with increasing sowing depth. Among the genes involved in diterpenoid biosynthesis processes, the expression levels of OsCYP76M5 and OsCYP71Z2 decreased, while OsCYP71Z21 increased with increasing sowing depth. Furthermore, the expression levels of these genes were analyzed using quantitative real-time polymerase chain reaction, which confirmed the transcriptome analysis results. Conclusions: This study identified candidate genes governing rice mesocotyl length and provides novel insights into the molecular regulatory mechanisms underlying mesocotyl elongation in rice. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box annotations for six complex behaviors across diverse habitat scenes. Leveraging this data, we propose AviaTAD-LGH, a real-time multi-task framework that incorporates auxiliary motion supervision into a dual-pathway 3D backbone to enhance feature discriminability. A critical bottleneck in such multi-task settings is the negative transfer caused by conflicting optimization objectives. To resolve this, we present Lightweight Gradient Harmonization (LGH), a plug-and-play optimization strategy that dynamically modulates task weights based on the cosine similarity of gradient directions. This mechanism effectively aligns optimization trajectories without introducing inference latency. Extensive experiments demonstrate that AviaTAD-LGH achieves a state-of-the-art mAP of 68.60%, surpassing strong public baselines by 7.44% and improving upon the single-task baseline by 2.80%, with significant gains observed on ambiguous dynamic classes. The proposed pipeline enables efficient, scalable ecological monitoring suitable for edge deployment. Full article

Reliable semantic perception is crucial for service robots operating in complex public indoor environments. However, existing semantic mapping approaches often face the dual challenges of high computational overhead and semantic redundancy in maps. To address these limitations, this paper proposes a low-resource semantic mapping framework based on improved instance segmentation and dynamic constraints from consecutive frames. First, we design the lightweight model MS-YOLO, which adopts MobileNetV4 as its backbone network and incorporates the SHViT neck module, effectively optimizing the balance between detection accuracy and computational cost. Second, we propose a consecutive frame dynamic constraint method that eliminates redundant object annotations through consecutive frame stability verification. Experimental results relating to both fusion and custom datasets demonstrate that compared to YOLOv8n-seg, MS-YOLO achieves improvements in accuracy, recall, and mAP@0.5, while reducing the number of parameters by 11.7% and floating-point operations (FLOPs) by 32.2%. Furthermore, compared to YOLOv11n-seg and YOLOv5n-seg, its FLOPs are reduced by 17.2% and 25.5%, respectively. Finally, the successful deployment and field validation of this system on the Jetson Orin NX platform demonstrate its real-time capability and engineering practicality for edge computing in public indoor service robots. Full article

The global rise in electronic waste (e-waste), especially in semiconductor components such as circuit boards and microchips, underscores a critical need for improved recycling technology. Current industrial sorters often miss small, high-value components. This leads to the loss of precious metals and inefficient recycling processes. This paper introduces an automated detection framework for detecting semiconductor components in e-waste. It assesses ensemble learning methods that leverage the strengths of multiple YOLO (You Only Look Once) object detection models, including YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12. Three ensemble fusion strategies are systematically compared: standard Non-Maximum Suppression (NMS), voting-based strategies (Affirmative, Consensus, Unanimous), and Weighted Box Fusion (WBF) with both static and dynamic weight optimization. Our simulations demonstrate that using multiple models together is far more effective than a single model for the following reasons. 1. Higher Accuracy: The best configuration, Top-4 Consensus Voting ensemble strategy, achieved an mAP@0.5 of 59.63%, a 10.3% improvement over the best individual model (YOLOv8s, 54.04%); 2. Greater Reliability: It significantly reduced “false negatives” (missed detections), even in cluttered or crowded e-waste scenarios; 3. Enhanced Detection: While the individual YOLOv8 model is fast (taking only 62.6 ms), supporting real-time detection, the best ensemble configuration (Consensus Top-4) takes 384.9 ms, creating a trade-off between detection accuracy and speed; 4. Well-Balanced Performance: Some fusion strategies showed slight trade-offs in mAP for certain parts, but collectively achieved a 7% rise in F1-score, indicating a better balance between precision and recall. This research marks significant progress in smart recycling. Improved component identification allows for more efficient recovery of high-purity materials. This promotes a circular economy by ensuring that rare and strategic materials in electronics are reused instead of discarded. Full article

Object detection in UAV-based fire rescue scenarios faces multiple challenges, including densely distributed small targets, severe occlusion, and interference from smoke and flames. Existing mainstream detection models, such as the YOLO series, often prioritize inference speed at the expense of modeling global context and spatial positional information, resulting in limited performance in such complex environments. To address these limitations, this paper proposes FirePM-YOLO, an object detection architecture optimized for fire rescue applications. Based on the YOLO framework, the proposed model introduces two key innovations: first, a Position-Aware Enhanced Mamba module (PEMamba) is designed, which incorporates a compact positional encoding mechanism, lightweight spatial enhancement, and an adaptive feature fusion strategy to significantly improve scene perception while maintaining computational efficiency. Second, a PEMBottleneck structure is constructed, which dynamically balances local convolutional features and global PEMamba features via learnable weights. This module is embedded into the shallow layers of the backbone network, forming an enhanced PEM-C3K2 module that captures long-range dependencies with linear complexity while preserving fine local details, thereby enabling holistic contextual understanding of fireground environments. Experimental results on the self-built “FireRescue” dataset demonstrate that compared with the original YOLOv12 and other mainstream detectors, the proposed model achieves improvements in both mean average precision (mAP) and recall while maintaining real-time inference capability. Notably, it exhibits superior detection performance on challenging samples, such as small-scale and partially occluded professional firefighting vehicles. Full article

Rural road perception for agricultural machinery automation faces challenges including complex backgrounds, drastic lighting and weather variations, frequent occlusions, and high densities of small objects with significant scale variations. These factors make conventional detectors prone to missed detections and misclassifications. To address these issues, a 4K rural road dataset with 4771 images is constructed. The dataset covers 13 object categories and includes diverse day/night conditions and multiple weather scenarios on both structured and unstructured roads. EMAF-Net, a lightweight single-stage detector based on YOLOv4-P6, is proposed. The backbone integrates an EMHA module combining EfficientNet-B1 with multi-head self-attention (MHSA) for enhanced global context modeling while preserving efficient local feature extraction. The neck adopts an Improved ASPP and a bidirectional FPN to achieve robust multi-scale feature fusion and expanded receptive fields. Meanwhile, CIoU loss is used to optimize bounding box regression accuracy. The experimental results demonstrate that EMAF-Net achieves an mAP@0.5 of 64.05% and an mAP@0.5:0.95 of 48.95% on a rural road dataset. At the same time, it maintains a lightweight design with 18.3 M parameters and a computational complexity of 38.5 GFLOPs. Ablation studies confirm the EMHA module contributes a 6.22% mAP@0.5 improvement, validating EMAF-Net’s effectiveness for real-time rural road perception in autonomous agricultural systems. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

Melissopalynological analysis is essential for determining the botanical origin of honey, corbicular pollen and bee bread, as well as detecting adulteration. However, it traditionally relies on labor-intensive and subjective manual pollen identification. As a proof-of-concept preceding full honey analysis, this study evaluates artificial intelligence methods for automated pollen grain recognition under controlled conditions. Hazel (Corylus avellana L.) and dandelion (Taraxacum officinale F.H. Wigg.) were used as model taxa to validate the proposed approach before its application to real varietal honey samples. This study introduces a novel three-stage pipeline that decouples object detection from feature extraction, utilizing YOLOv12m for region-of-interest generation and, for the first time in melissopalynology, DINOv3 ConvNeXt-B for deep feature representation. Microscopic images acquired at 400× magnification yielded 2498 dandelion and 1941 hazel pollen grains. The detector achieved an mAP@0.5 of 0.936 with an F1 score of 0.88, while the classifier reached 98.1% accuracy with good class separability (Silhouette coefficient: 0.407). The primary technical contribution is the systematic optimization of the detection-to-classification interface. Context-aware bounding box expansion (12%) and an optimized IoU-NMS threshold (0.65) significantly improve the stability of morphological feature extraction, as confirmed by ablation studies. Computational cost reporting further supports reproducible, deployment-oriented comparison. The results confirm the feasibility of this AI-based framework as an intermediate step toward automated melissopalynological analysis, with future work focusing on standardized microscopy protocols and expanded pollen databases for varietal honey authentication. Full article

Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article

Safety monitoring in container hoisting operations within rail-road intermodal logistics parks is a critical task in industrial safety management. Such scenarios are characterized by complex environments, large variations in target scales, deformable object shapes, and frequent occlusions, which pose significant challenges to visual perception systems. Conventional single-task models suffer from inherent limitations in handling low recall rates for distant small targets and insufficient adaptability to geometric deformations, making them inadequate for high-precision, real-time safety warning applications. To address these challenges, this study proposes a unified visual analysis framework that integrates semantic segmentation and object detection to enhance the recognition performance of small and deformable targets in complex operational environments, enabling real-time perception and safety warning of key objects and hazardous regions within container yards. Specifically, we introduce FSD-YOLO, a fusion-based architecture composed of the following key components. First, a SegFormer-based semantic segmentation module is employed to achieve pixel-level delineation of different operational regions. Second, an improved object detection network is developed based on the YOLOv8n architecture, incorporating: (1) the integration of C2f modules in the shallow layers of the backbone to enhance high-resolution feature extraction; (2) the embedding of C2fDCN modules within the detection head to improve modeling capability for deformable objects via deformable convolution; (3) the adoption of CARAFE upsampling operators to optimize multi-scale feature fusion; and (4) a dynamic loss-weighting strategy for small objects, where loss weights are adaptively adjusted according to target area to increase training emphasis on small-scale targets. Finally, a decision-level fusion strategy is applied to combine segmentation and detection outputs, enabling real-time safety judgment based on semantic rules. Experimental results on a self-constructed container yard dataset demonstrate that the proposed detection model achieves an mAP50-95 of 0.6433 and an mAP50 of 0.9565, significantly outperforming the baseline YOLOv8n model (mAP50-95: 0.5394, mAP50: 0.8435), thereby validating the effectiveness of the proposed framework. Full article

Background/Objectives: Long-term exposure to high-risk human papilloma virus (HPV) leads to high-grade squamous intraepithelial lesions (HSILs), which may develop into cancer. Various proteins and metabolites change during the development of cervical cancer; thus, assessing the dysregulated molecules and pathways in HSILs is important to elucidate early pathological mechanisms and identify potential intervention targets. Methods: In this study, we performed proteomic and metabolomic analyses in five pairs of HPV-positive HSIL tissues and paired normal tissues. Immunohistochemistry (IHC) was applied to validate the levels of carnitine palmitoyltransferase 1A (CPT1A) in HSIL tissues. Quantitative real-time PCR and Western blot were used to detect the expression levels of CPT1A in cervical cancer cell lines. Results: In proteomic analysis, 836 proteins showed significant changes. Functional analyses of the differentially expressed proteins indicated that metabolic pathways, oxidative phosphorylation and ribosome are the top three enriched pathways. In metabolomic analysis, 105 metabolites were differentially altered. Most metabolites were involved in lipid metabolism, such as phosphatidylethanolamine (PE), phosphatidylinositol (PI) and L-palmitoylcarnitine. Integrated proteomics and metabolomics revealed that the metabolic pathway was the most enriched pathway that contained the maximum number of differentially expressed metabolites and proteins. In vitro, we found CPT1A was upregulated in HSIL tissues and in cervical cancer cell lines. Conclusions: Our findings characterize the protein and metabolite alterations in HSILs, which may represent molecular features associated with disease progression. Full article