Search Results (722)

Background/Objectives: Peri-implantitis is a major complication in implant dentistry, and its radiographic diagnosis remains challenging because conventional assessment is operator-dependent and bone loss is often detected only after measurable changes occur. Artificial intelligence (AI) may support the detection, quantification, and prognostic assessment of peri-implant bone conditions. This review aimed to synthesize evidence on AI-based radiographic approaches for peri-implantitis detection, marginal bone loss measurement, and risk stratification. Methods: PubMed and Scopus were searched for original studies published between 2013 and 2025 that applied artificial intelligence (AI), including machine learning and deep learning, to peri-implantitis. Eligible studies focused on peri-implant bone assessment and reported quantitative performance metrics. Extracted data included imaging modality, AI model, task, dataset, reference standard, validation strategy, performance, and clinical relevance. A qualitative synthesis was performed. Results: Eleven studies met the eligibility criteria; however, one full text could not be retrieved, and ten studies were included. In most of the studies, peri-implant marginal bone loss detection or measurement was performed using periapical/intraoral radiographs, while only few studies used panoramic or combined imaging. Common architectures included YOLO variants, Faster R-CNN, Mask R-CNN, U-Net, ResNet, and AlexNet. Performance was generally encouraging for implant localization, bone loss detection, keypoint identification, and severity classification. Only one study addressed outcome prediction. All studies were retrospective and internally validated. Conclusions: AI may support radiographic detection and quantification of peri-implant bone loss as an adjunctive diagnostic tool. However, evidence is limited by retrospective designs, heterogeneous reference standards, lack of external validation, and limited clinical-data integration. Future studies should prioritize prospective multicenter validation, longitudinal imaging, and multimodal models. Full article

Designers, engineers, and building officials face increasing pressure to accelerate and improve the accuracy of design review for buildings and infrastructure. Roof assemblies and rooftop structures are particularly challenging due to the complexity and fragmentation of regulatory requirements, especially in jurisdictions such as Florida, where compliance must be verified across both the residential and commercial volumes of the Florida Building Code (FBC). The resulting review process is technically demanding and time-intensive, imposing significant cognitive and operational burdens on practitioners and under-resourced public agencies. To address these challenges, this study proposes and evaluates an agentic artificial intelligence (AI) framework for automated code compliance checking of roof assemblies and rooftop structures. The framework employs a multi-agent architecture in which specialized AI agents collaboratively interpret regulatory provisions and evaluate roof design parameters across four core modules: data preprocessing and code ingestion, rule-based and semantic analysis, results visualization, and iterative validation. YOLO11m-seg and Mask R-CNN were used for element detection and segmentation, and the system was developed using 150 design projects, including roof plans, section details, and specifications. Four large language models from two families (Mistral and GPT) were comparatively evaluated on standardized compliance tasks. The framework was then tested on a held-out portfolio of 15 distinct roof-design projects comprising 60 code-compliance decisions derived from the FBC 2023, with performance measured by precision, recall, F1-score, and accuracy. GPT-5.4 achieved the highest overall performance (F1 = 0.97; accuracy = 97%). Because the reasoning and vision components were evaluated separately rather than as an integrated end-to-end pipeline, and the scope was limited to one jurisdiction and two drawing types, broader code coverage and production-setting validation are needed before claims of generality. Nonetheless, the results suggest that agentic AI can meaningfully support compliance review and reduce reviewer burden in roof-design permitting. Full article

Elkhorn coral (Acropora palmata) is a threatened reef-building species that plays a critical role in Caribbean coastal ecosystems. Efficient, large-scale monitoring of A. palmata is essential for evaluating restoration success, yet traditional in situ surveys remain costly and spatially constrained. In this study, we acquired high-resolution (1.8 cm) uncrewed aerial vehicle (UAV) imagery of a coral reef within the United States Virgin Islands’ (USVI) St. Croix East End Marine Park (STXEEMP) and applied deep learning object detection to identify individual A. palmata colonies. We utilized two convolutional neural network architectures, FasterRCNN and MaskRCNN. FasterRCNN was used as an initial screening tool to identify the optimal imagery dataset from several candidates. After identifying the dataset, we used MaskRCNN with an iterative annotation refinement procedure in which initial model predictions were used to augment the training data and achieved an F1 score of 0.78. Detection accuracy was strongly influenced by colony size and apparent water depth, with markedly high accuracy for corals wider than 0.3 m (F1 = 0.87) and located in shallower waters (F1 = 0.81). Beyond detection, MaskRCNN’s polygon outputs enabled the measurement of the individual colony area and the generation of high-resolution coral density maps. These products complement broader-scale prediction and mapping approaches and provide fine-scale, management-relevant information. Although this study was conducted at a single reef site during one acquisition period, the results suggest that UAV-based deep learning workflows offer a promising approach for coral reef monitoring that could support restoration assessments and conservation decision-making, pending validation across additional sites, seasons, and environmental conditions. Full article

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

Fish phenotyping plays an important role in growth evaluation, selective breeding, and precision aquaculture. Conventional phenotypic measurement methods are labor-intensive, time-consuming, and susceptible to observer variability. To improve measurement efficiency and reproducibility, this study proposes an automated fish phenotyping framework based on Transformer-enhanced instance segmentation. Specifically, a Mask2Former decoder was integrated into the Mask R-CNN architecture to improve boundary delineation and segmentation quality. Based on segmentation outputs, phenotypic parameters, including body length, body height, and projected area, were automatically extracted using PCA-assisted orientation estimation and geometric measurement. In addition, a standardized anatomical landmark annotation framework consisting of 12 reference points was introduced to support reproducible phenotypic description and future extensible morphometric analysis. Body weight was further estimated using polynomial regression based on extracted morphological traits. Experiments were conducted using images from three crucian carp varieties under controlled imaging conditions. The proposed framework achieved 92.7% mAP and 89.4% Boundary IoU, improving segmentation performance over the baseline model. Automated measurement yielded average relative errors of 2.16% for body length and 3.85% for body height, while weight prediction achieved an R² of 0.9479 and a mean relative error of 7.31%. These results demonstrate that Transformer-enhanced segmentation can support accurate and efficient automated phenotyping under standardized conditions and provide a foundation for future deployment in more complex aquaculture environments. Full article

Solar filaments (and their limb counterparts, prominences) are critical tracers of the Sun’s magnetic topology and key precursors to coronal mass ejections (CMEs). Precise identification and continuous tracking of these features are essential for understanding solar eruptive mechanisms and improving space weather forecasting. This systematic review evaluates the evolution of automated detection methodologies, addressing the challenge of processing the exponentially growing volume of high-resolution solar observations. We identify deep learning architectures, particularly U-Net variants and Mask R-CNN, as the most promising current paradigms. Compared to traditional image processing, these data-driven models demonstrate superior robustness against noise and variable observing conditions, achieving high-precision segmentation (>90% accuracy) with sub-second inference speeds. This leap in computational efficiency and accuracy directly facilitates real-time operational monitoring and enables large-scale statistical analysis of filament evolution across solar cycles. We conclude that future breakthroughs lie in developing physics-informed AI and standardized benchmarks to bridge the gap between pixel-level segmentation and physical interpretation, ultimately creating detection systems that are both operationally reliable and scientifically meaningful. Full article

Building instance segmentation in remote sensing imagery supports applications such as urban management, disaster assessment, 3D urban modeling, and land-cover monitoring. However, variations in building scale, dense spatial distribution, complex background textures, shadows, and occlusions make it difficult to balance segmentation accuracy, boundary recovery, inference efficiency, and deployment cost. This study establishes a unified multidimensional benchmark for remote sensing building instance segmentation. The primary benchmark evaluates mask-predicting instance segmentation models, including YOLOv8-seg, YOLOv11-seg, YOLO26-seg, and Mask R-CNN, under consistent training and evaluation settings. RT-DETR-l and RT-DETR-x are retained only as auxiliary detection-only Transformer baselines because they do not output instance masks in the implemented setting. The benchmark covers bounding-box detection, mask-based segmentation, inference efficiency, model complexity, training behavior, and qualitative visualization. To assess cross-dataset transferability and degradation-specific robustness beyond a single dataset, we further conduct zero-shot WHU-to-Inria testing, independent Inria training/testing with different initialization strategies, and controlled degradation tests involving shadow/occlusion and Gaussian blur. Results on WHU and Inria show that high-capacity YOLO-seg models are competitive among the evaluated mask-predicting models. Under the current experimental settings, YOLOv11x-seg achieves the highest or near-highest mask-based accuracy, whereas YOLOv11m-seg provides a favorable balance between accuracy, speed, and complexity. The zero-shot WHU-to-Inria test reveals a clear domain shift, while the Inria in-domain experiments indicate that high-capacity YOLO-seg models recover competitive performance after target-domain training. The controlled degradation tests show a smaller performance drop under shadow/occlusion than under Gaussian blur for YOLOv11x-seg. These findings provide benchmark-specific evidence for selecting remote sensing building instance segmentation models under accuracy-oriented and efficiency-oriented deployment requirements. Full article

Berry thinning is a fundamental operation in modern vineyard management, and future robotic thinning systems have the potential to reduce labor intensity and improve operational consistency. However, automated berry thinning under field conditions is still constrained by insufficient berry-level segmentation accuracy, difficulty in recognizing occluded berries, and high missed-detection rates for small berries. These limitations mainly arise from dense berry arrangements, severe mutual occlusion, and the subtle visual features of small targets. To address these challenges, this study developed a lightweight grape berry instance segmentation and thinning decision-support method based on YOLOv8n-seg. A two-stage knowledge distillation strategy, using Mask R-CNN and YOLOv8l-seg as teacher models, was combined with 30% backbone pruning to improve the recognition of occluded and small berries while maintaining model efficiency. Subsequently, the DBSCAN clustering algorithm was used to analyze berry centroid coordinates and equivalent diameters extracted from instance segmentation masks, thereby generating preliminary thinning-target recommendations based on local berry density and berry size. The model was trained and evaluated on a self-constructed dataset containing 330 valid grape bunch images collected in 2025 from Yongming Vineyard, Lin’an District, Hangzhou, Zhejiang Province, China. The results showed that the optimized YOLOv8n-seg model achieved a box $mAP50\text{-}95$ of 0.8945 and a mask $mAP50\text{-}95$ of 0.7910, with an inference speed of 119.19 $FPS$ and 3.26 M parameters on an NVIDIA RTX 3060 Laptop GPU. Compared with the original YOLOv8n-seg model, the optimized model improved mask $mAP50\text{-}95$ by 1.20 percentage points, increased inference speed by 71.79 $FPS$, and reduced the number of parameters by 2.38 M. These results indicate that the proposed method improves grape berry instance segmentation performance while achieving a favorable balance among segmentation accuracy, lightweight characteristics, and inference efficiency. The proposed framework provides an offline RGB-based visual perception and preliminary thinning decision-support method for future grape berry thinning robots. However, because the current dataset was collected from Shine Muscat grape bunches at the berry enlargement stage in a single vineyard using the same imaging setup, the results should be interpreted as preliminary evidence under the specific cultivar, growth stage, vineyard, and imaging conditions of this study. Further validation across different grape cultivars, growth stages, vineyards, production seasons, camera systems, embedded platforms, and real robotic thinning operations is still required. Full article

Background: Accurate segmentation of white blood cells (WBCs) in peripheral blood smear images is a fundamental step in computational hematology, enabling downstream tasks such as classification, morphological assessment, and quantitative analysis. However, reliable segmentation remains challenging due to staining variability, complex cellular morphology, overlapping structures, and limited availability of high-quality annotations. Aim and Methods: The aim of this study is to develop a robust and fully automated segmentation framework for white blood cells (WBCs) in microscopic blood smear images, providing a reliable foundation for subsequent computational analysis. We propose TransNet–SAM2, a hybrid deep learning architecture that integrates hierarchical transformer-based feature extraction with a foundation-model-based decoder for prompt-free segmentation. Specifically, a Swin Transformer backbone is employed to capture multi-scale contextual representations, which are subsequently aligned and fused through a feature adaptation module. The fused features are directly injected into the SAM2 mask decoder, replacing conventional prompt-based conditioning and enabling fully automatic segmentation. In addition, a weakly supervised self-training strategy is incorporated to utilize partially annotated data, improving model generalization while reducing annotation requirements. The proposed framework is evaluated using a clinically curated dataset from Dicle University, the publicly available Raabin-WBC dataset, and an additional external leukemic blast validation dataset (ALL-IDB) to assess robustness under both routine and atypical hematological conditions. Results: TransNet-SAM2 achieved a Dice coefficient of 0.95 ± 0.01 and IoU of 0.90 on internal testing, significantly outperforming U-Net (0.89), Mask R-CNN (0.90), and SAM2 (0.92) (p < 0.05). In cross-dataset evaluation (Dicle training, Raabin-WBC testing), the framework maintained strong performance (Dice: 0.91, IoU: 0.84), demonstrating robustness to domain shifts. Ablation studies confirmed each component’s contribution, with the full model improving Dice by 6% over a CNN baseline. Qualitative analysis showed accurate boundary delineation even with cell overlap and background clutter. Conclusions: These findings indicate that the proposed framework provides a promising and scalable framework for WBC segmentation. While the current study focuses on segmentation, future work will investigate integration with classification and radiomics pipelines, as well as validation on more diverse clinical datasets, including bone marrow and leukemia samples. Full article

The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains challenging because of blurred boundaries, object adhesion, missed detections, and inaccurate mask delineation in field environments. To improve sapling segmentation performance and address these challenges, this study proposes a multimodal Mask R-CNN framework in which RGB imagery was paired with one multispectral-derived vegetation index at a time to construct separate RGB-VI input combinations, taking ginkgo saplings as a representative case. A dataset of 400 saplings was constructed using a high-throughput field phenotyping platform. The backbone network was extended with an independent vegetation index branch, and three fusion strategies (early, multi-step, and late fusion) were designed within a feature pyramid network to enable multi-scale multimodal feature integration. The results showed that all multimodal models outperformed unimodal baselines in terms of segmentation accuracy and recall. Among them, the multi-step fusion strategy achieved the best performance, while the RGB-EVI multi-step fusion model achieved the highest strict-matching precision (AP@75 = 87.7%) and recall (71.3%), with superior performance in dense sapling delineation and background suppression. These findings indicate that multimodal feature fusion can effectively improve sapling instance segmentation and provide methodological support for high-throughput plant phenotyping. Full article

Traditional rectal temperature measurement in pigs induces stress in animals, imposes a heavy labor burden on staff, and increases the risk of cross-infection. This study proposes a non-invasive deep learning approach to predict porcine rectal temperature by combining infrared thermal images of thermal windows with environmental parameters. A multimodal dataset is constructed by synchronously collecting thermal images, environmental parameters, and actual rectal temperatures. Mask Region-based Convolutional Neural Network (Mask R-CNN), You Only Look Once version 8 small (YOLOv8s), and YOLOv11s are employed to automatically detect or segment thermal window regions, from which the maximum temperature of each region is extracted. To enhance model generalization under varying environmental conditions, a two-stage hybrid regression framework is established. In this framework, a Convolutional Neural Network (CNN) extracts spatial features from thermal images, a fully connected network (FCNN) encodes regional surface temperatures and environmental parameters, and a Transformer module captures cross-modal dependencies to generate a preliminary prediction. Subsequently, a Random Forest (RF) regressor is applied for residual correction and final output optimization. Comparative experiments on single-region, dual-region, and triple-region combinations demonstrate that the “eye + vulva” dual-region scheme yields the optimal performance, with a mean absolute error (MAE) of 0.1796 °C and a coefficient of determination (R²) of 0.8212. The prediction error of this scheme is reduced by 42.3% compared with the best-performing unimodal model. The proposed method provides a fast, accurate, and stress-free solution for porcine body temperature monitoring, thereby supporting the development of intelligent health management in livestock farming. Full article

To address the issues of traditional high-resolution spatial remote sensing cameras—complex optical systems, heavy weight, long development cycles, and high costs—this study combines the optical design parameters and product characteristics of lightweight remote sensing payloads. Based on the “physical simplification–algorithm enhancement” computational imaging paradigm, an algorithm-side enhancement technical system tailored to these lightweight payloads is constructed. This paper establishes a point-spread function (PSF) model for simplified optical systems and a dedicated imaging degradation model, verifying the compensation mechanism of computational methods against optical degradation effects. It achieves high-performance imaging through “low-precision simplified optics + high-precision algorithms,” providing theoretical support and practical implementation pathways for lightweight, low-cost, and rapid-response spaceborne remote sensing payloads. Experimental results confirm the excellent imaging performance of the camera, validating the effectiveness of the proposed optical design. Compared with the baseline Mask R-CNN (region-convolution neural networks), the AP50 and overall AP (average precision) of the AS Mask R-CNN are improved by 4.0% and 1.0%, respectively. This research offers a robust technical solution for intelligent remote sensing camera modes and serves as valuable reference and technical support for the opto-mechanical co-design of high-resolution remote sensing payloads. Full article

Objectives: This study aimed to evaluate the influence of panoramic radiograph quality on the performance of an artificial intelligence (AI)-based tooth detection system and to identify specific image quality criteria associated with detection accuracy. Methods: A total of 424 panoramic radiographs were retrospectively selected from a clinical database. Radiographic quality was assessed using a modified Clinical Image Evaluation Chart, including criteria related to bite block presence, anteroposterior positioning, occlusal plane curvature, patient movement, anatomical coverage, overlapping contact points, air gap, contrast, cervical spine overlap, symmetry of the ascending mandibular ramus, and the number of visible teeth. Automated tooth detection was performed using a convolutional neural network based on the Mask R-CNN architecture (SynbrAIn, Italy). AI detection outputs were validated against expert human evaluation. Spearman’s rank correlation analyses were conducted to assess associations between individual image quality criteria and the number of AI detection errors per radiograph. Results: Significant negative associations were observed between AI detection errors and the number of visible teeth (ρ = −0.311, p < 0.001), presence of a bite block (ρ = −0.248, p < 0.001), reduced patient movement (ρ = −0.204, p < 0.001), correct anteroposterior positioning (ρ = −0.165, p < 0.001), and overall image quality score (ρ = −0.120, p = 0.010). In contrast, the presence of an air gap above the anterior teeth (ρ = 0.099, p = 0.042) and overlapping contact points (ρ = 0.122, p = 0.012) were positively associated with increased detection errors. No significant associations were identified for occlusal plane curvature, contrast, cervical spine overlap, anatomical coverage, or mandibular ramus symmetry. Overall, the AI system was more sensitive to indicators of anatomical completeness and patient positioning than to minor radiographic imperfections. Conclusions: Panoramic radiograph quality, particularly indicators of anatomical completeness and patient positioning, is associated with the performance of AI-based tooth detection. While the AI system demonstrated robustness to common image quality variations, adherence to standardized acquisition protocols remains important to minimize detection errors. Full article

Weed infestation remains a major constraint in wheat production, highlighting the need for accurate and scalable monitoring approaches. Recent advances in unmanned aerial vehicle (UAV) and deep learning (DL) have created new opportunities for field-scale weed detection. This study evaluates the performance of three DL models (Mask R-CNN, YOLO26s-seg, and RT-DETR-L) for detecting common weed species in a 4-hectare rain-fed wheat field in Mongolia using UAV-acquired images. The following three weed species were identified as the most abundant under the dry environmental conditions of 2025: Linaria buriatica, Neneo pulla, and Artemisia scoparia. The models were evaluated using Precision, Recall, and F1-score. The detection F1-score for the L. buriatica, N. pulla and A. scoparia was 0.642, 0.568 and 0.163 for Mask R-CNN; 0.615, 0.655, 0.335 for YOLO26s-seg; and 0.693, 0.647, 0.275 for RT-DETR-L, respectively. Segmentation F1-score values were 0.659, 0.600, 0.179 for Mask R-CNN, whereas YOLO26s-seg achieved 0.613, 0.658, 0.326, respectively. RT-DETR-L achieved the best overall detection performance for the dominant species, while YOLO26s-seg showed the strongest detection of minority species, and Mask R-CNN produced the most accurate segmentation boundaries. Despite the imbalance in weed instances their morphological characteristics influenced the performance of the model. These findings demonstrate the feasibility of UAV imagery and DL models in precise weed management practices in Mongolia. Full article

Forest ecosystems in northeastern Japan are characterized by natural mixed forests, where sustainable management has always been limited because of their difficult accessibility. The aims of this study are first, to assess mixed forest composition, and second, to train Mask R-CNN models with these data in order to detect and segment trees in a 19-ha mixed forest composed mainly of beech (Fagus crenata), oak (Quercus crispula), magnolia (Magnolia obovata) and larch (Larix kaempferi). The Mask R-CNN model was applied in two experimental scenarios: a single multi-class model and species-specific models. RGB images consisted of four orthomosaics (August, September, October 2024 and October 2025), which yielded 1725, 359, 129 and 525 samples of each tree species, respectively. The Unmanned Aerial Vehicle (UAV)-QField validation method improved the classification accuracy of the annotations and made it possible to map each tree species distribution and understand the composition of mixed forests along an elevation gradient. The multi-class model demonstrated an overall precision of 0.59, a recall of 0.53, and an F1-score of 0.56. The detection performance for individual tree species was similar for both models. Based on these results, the multi-class model is more suitable because it decreases the possibility of misclassification of tree species. Full article