Search Results (356)

This review deals with the issue of operational coal quality control using instrumental nuclear–physical methods. The existing traditional method of coal testing, characterized by high labor intensity and low representativeness, cannot serve as a basis for operational management of mining and processing processes. Instrumental nuclear–physical methods are free from these drawbacks; they are based on various processes of interaction of gamma and neutron radiation with substances. The main modifications of instrumental methods using gamma radiation are discussed: backscattering, forward gamma scattering, gamma absorption, gamma annihilation, and natural gamma activity. Various modifications of gamma methods are related to the energy of the primary and recorded radiation, the prevalence of a particular interaction process, the depth of the method, characteristics of the test object, the measurement geometry, and the other factors. The features of gamma methods are described in the context of the tasks being solved, interfering factors (variations in the bulk density, the moisture content, and the elemental composition), and methodological approaches for increasing the sensitivity and accuracy of the coal quality assessment. The variety of modifications of neutron methods is associated with irradiation of the analyzed coal with neutrons of different energies and detection of secondary gamma radiation arising from neutron activation of elements, inelastic scattering of fast neutrons, and radiative capture of thermal neutrons by the elements composing the coal. The methodological features of neutron activation, the neutron–gamma method of inelastic scattering and radiative capture are considered in the context of elemental analysis for Al, Si, S, Ca, Fe, H, C, and O and determining the ash content of coal in general. The main trends of the instrumental quality control are highlighted and recommendations are given for their use depending on the metrological characteristics and physical and chemical properties of the control object. The gamma-albedo method with registration of X-ray fluorescence of heavy gold-forming elements is the most promising for express analysis of powder samples. To test coarse coal in large amounts, multiparameter neutron methods are needed that comprehensively utilize high-precision equipment and instrumental signals from carbon, oxygen, and major ash-forming elements. Full article

Background/Objectives: Vision–language models such as BiomedCLIP are increasingly investigated for their diagnostic potential in medical imaging. Although these foundation models show promise in general radiographic interpretation, their application in pediatric domains—particularly for subtle, postoperative findings like esophageal strictures—remains underexplored. This study aimed to evaluate the diagnostic performance of BiomedCLIP in classifying pediatric esophageal radiographs into three clinically relevant categories: presence of contrast agent, full esophageal visibility, and presence of esophageal stricture. Methods: We retrospectively analyzed 143 pediatric esophageal X-rays collected between 2021 and 2025. Each image was annotated by two pediatric radiology experts and categorized according to esophageal visibility, contrast presence, and stricture occurrence. BiomedCLIP was used in a zero-shot classification setup without fine-tuning. Model predictions were converted into binary outcomes and assessed against the ground truth using a comprehensive suite of 27 performance metrics, including accuracy, sensitivity, specificity, F1-score, AUC, and calibration analyses. Results: BiomedCLIP achieved high precision (88.7%) and a favorable AUC (85.4%) in detecting contrast agent presence, though specificity remained low (20%), leading to a high false-positive rate. The model correctly identified all cases of non-visible esophagus, but was untestable in predicting full visibility due to the absence of positive cases. Critically, its performance in detecting esophageal strictures was poor, with accuracy at 24%, sensitivity at 44%, specificity at 18%, and AUC of 0.26. Statistical overlap between contrast and stricture predictions indicated a lack of semantic differentiation within the model’s latent space. Conclusions: BiomedCLIP shows potential in detecting high-salience features such as contrast but fails to reliably identify esophageal strictures. Limitations include class imbalance, absence of fine-tuning, and architectural constraints in recognizing subtle morphologic abnormalities. These findings emphasize the need for domain-specific adaptation of foundation models before clinical implementation in pediatric radiology. Full article

In recent years, digital technologies have become increasingly prevalent in the field of heritage protection. In addition to geomatic techniques like laser scanning (LiDAR) and Structure-from-Motion (SfM), geophysical methods, especially Ground-Penetrating Radar (GPR), offer added value for investigating protected buildings and objects. Additionally, chemical analysis (e.g., X-ray fluorescence, XRF) and mineral magnetic methods can be utilized to investigate specific research topics. All these methods are completely non-invasive and leave the heritage site untouched. Furthermore, they are cost-efficient and fast to use. Within this paper, we want to present an integrated study of a medieval sarcophagus in Bamberg Cathedral. The geophysical surveys via GPR and magnetic susceptibility (MS) measurements should answer open questions regarding the construction and internal layout of the sandstone sarcophagus, dated to the Early or High Middle Ages. The susceptibility data indicated an inner lead coffin in the lower part behind the stone slabs due to an unusual diamagnetic response in these parts. In contrast, the GPR data gave no such indication and revealed that the interior is too small for a direct burial of the bishop. Hence, an additional XRF survey was conducted to help solve this contradiction. The latter data indicate that the lead could be due to remains of a former painting on the sarcophagus with colours containing lead white pigments. Due to the porous sandstone, the moist environmental conditions, and the high weight of the lead elements, these could have accumulated at the bottom of the sarcophagus, creating the diamagnetism detected by the magnetic susceptibility measurements. Full article

Background and Objectives: Hip joint disorders exhibit diverse and overlapping radiological features, complicating early diagnosis and limiting the diagnostic value of single-modality imaging. Isolated imaging or clinical data may therefore inadequately represent disease-specific pathological characteristics. Materials and Methods: This retrospective study included 605 hip joints from Center A (2018–2024), comprising normal hips, osteoarthritis, osteonecrosis of the femoral head (ONFH), and femoroacetabular impingement (FAI). An independent cohort of 24 hips from Center B (2024–2025) was used for external validation. A multimodal deep learning framework was developed to jointly analyze radiographs, CT volumes, and clinical texts. Features were extracted using ResNet50, 3D-ResNet50, and a pretrained BERT model, followed by attention-based fusion for four-class classification. Results: The combined Clinical+X-ray+CT model achieved an AUC of 0.949 on the internal test set, outperforming all single-modality models. Improvements were consistently observed in accuracy, sensitivity, specificity, and decision curve analysis. Grad-CAM visualizations confirmed that the model attended to clinically relevant anatomical regions. Conclusions: Attention-based multimodal feature fusion substantially improves diagnostic performance for hip joint diseases, providing an interpretable and clinically applicable framework for early detection and precise classification in orthopedic imaging. Full article

Juvenile idiopathic arthritis (JIA) is the most prevalent chronic rheumatologic condition in childhood, with an incidence that continues to rise worldwide. Despite substantial progress in therapeutic strategies over the past two decades, JIA remains a major health concern. Beyond joint inflammation and musculoskeletal impairment, accumulating evidence indicates that JIA is associated with metabolic disturbances and altered body composition, which may predispose affected children to an elevated cardiovascular risk in the long term. The objective of this review is to synthesize current knowledge on these metabolic and anthropometric alterations and to evaluate the role of non-invasive diagnostic methods in detecting early cardiovascular changes. A narrative review of the literature was conducted using PubMed and Scopus databases, focusing on studies assessing lipid metabolism, insulin resistance, adiposity, and cardiovascular markers in pediatric patients with JIA. Special attention was given to non-invasive diagnostic approaches, including bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DXA), skinfold thickness, transient elastography, carotid intima–media thickness (cIMT), as well as selected biochemical markers. Evidence suggests that children with JIA frequently present with dyslipidemia, increased insulin resistance, and abnormal body fat distribution compared with their healthy peers. Non-invasive assessment methods, particularly DXA and cIMT, have demonstrated sensitivity in detecting subclinical metabolic and vascular changes. These alterations resemble early features of metabolic syndrome and are thought to contribute to premature cardiovascular morbidity in this population. Incorporating non-invasive cardiovascular risk assessment into routine rheumatology practice may improve early detection of metabolic and vascular complications in JIA, support timely preventive interventions, and ultimately enhance long-term outcomes for affected children. Most available evidence is derived from cross-sectional studies, highlighting the need for longitudinal investigations to better define long-term cardiometabolic risk in JIA. Full article

Background/Objectives: Interpretation of chest X-rays (CXRs) requires accurate identification of lesion presence, diagnosis, location, size, and number to be considered complete. However, the effectiveness of large language models with vision capabilities (LLMs) in performing these tasks remains uncertain. This study aimed to evaluate the image-only interpretation performance of LLMs in the absence of clinical information. Methods: A total of 247 CXRs covering 13 diagnostic categories, including pulmonary edema, cardiomegaly, lobar pneumonia, and other conditions, were evaluated using Gemini 1.0, Gemini 1.5 Pro, GPT-4 Turbo, and GPT-4o. The text outputs generated by the LLMs were evaluated at two levels: (1) primary diagnosis accuracy across the 13 predefined diagnostic categories, and (2) identification of key imaging features described in the generated text. Primary diagnosis accuracy was assessed based on whether the model correctly identified the target diagnostic category and was classified as fully correct, partially correct, or incorrect according to predefined clinical criteria. Non-diagnostic imaging features, such as posteroanterior and anteroposterior (PA/AP) views, side markers, foreign bodies, and devices, were recorded and analyzed separately rather than being incorporated into the primary diagnostic scoring. Results: When fully and partially correct responses were treated as successful detections, vLLMs showed higher sensitivity for large, bilateral, multiple lesions and prominent devices, including acute pulmonary edema, lobar pneumonia, multiple malignancies, massive pleural effusions, and pacemakers, all of which demonstrated statistically significant differences across categories in chi-square analyses. Feature descriptions varied among models, especially in PA/AP views and side markers, though central lines were partially recognized. Across the entire dataset, Gemini 1.5 Pro achieved the highest overall detection rate, followed by Gemini 1.0, GPT-4o, and GPT-4 Turbo. Conclusions: Although LLMs were able to identify certain diagnoses and key imaging features, their limitations in detecting small lesions, recognizing laterality, reasoning through differential diagnoses, and using domain-specific expressions indicate that CXR interpretation without textual cues still requires further improvement. Full article

This study investigates a 26th Dynasty Ptah–Sokar–Osiris wooden statuette excavated from the Tari cemetery, Giza Pyramids area, to decode ancient Egyptian manufacturing techniques and establish evidence-based conservation strategies of such wooden objects. Using minimal sampling (1.0–2.0 mm²), integrated XRF, synchrotron-based X-ray diffraction, FTIR, and confocal microscopy distinguished original technological choices from burial-induced alterations. The 85 cm Vachellia nilotica sculpture exhibits moderate structural preservation (cellulose crystallinity index 62.9%) with partial chemical deterioration (carbonyl index 2.22). Complete pigment characterization identified carbon black, Egyptian Blue (cuprorivaite, 55 ± 5 wt %), atacamite-dominated green (65 ± 5 wt %) with residual malachite (10 ± 2 wt %), orpiment (60 ± 5 wt %), red ochre (hematite, 60% ± 5 wt %), white pigments (93 ± 5 wt % calcite), and metallic gold (40 ± 5 wt %). Confocal microscopy revealed sophisticated multi-pigment mixing strategies, with black carbon systematically blended with chromophores for nuanced color effects. Atacamite predominance over malachite provides evidence for chloride-mediated diagenetic transformation over 2600 years of burial. Consistent calcite detection (~ 20–45%) across colored layers confirms systematic ground layer application, establishing technological baseline data for 26th Dynasty Lower Egyptian workshops. Near-complete organic binder loss, severe lignin oxidation, and ongoing salt-mediated mineral transformations indicate urgent conservation needs requiring specialized consolidants, paint layer stabilization, and controlled environmental storage. This investigation demonstrates synchrotron methods’ advantages while establishing a minimally invasive framework for studying polychrome wooden artifacts. Full article

Background/Objective: Pulmonary impairments have been identified as some of the most complex and debilitating post-acute sequelae of SARS-CoV-2 infection (PASC) or long COVID. This study identified and characterised the specific forms of pulmonary impairments detected using pulmonary function tests (PFT), chest X-rays (CXR), and computed tomography (CT) scans in patients with long COVID symptoms. Methods: We conducted a single-centre retrospective study to evaluate 60 patients with long COVID who underwent PFT, CXR, and CT scans. Pulmonary function in long COVID patients was assessed using defined thresholds for key test parameters, enabling categorisation into normal, restrictive, obstructive, and mixed lung-function patterns. We applied exact binomial (Clopper–Pearson) 95% confidence intervals to calculate the proportions of patients falling below the defined thresholds. We also assessed the relationships among spirometric indices, lung volumes, and diffusion capacity (DLCO) using scatter plots and corresponding linear regressions. The findings from the CXRs and CT scans were categorised, and their prevalence was calculated. Results: A total of 60 patients with long COVID symptoms (mean age 60 ± 13 years; 57% female) were evaluated. The cohort was ethnically diverse and predominantly non-smokers, with a mean BMI of 32.4 ± 6.3 kg/m². PFT revealed that most patients had preserved spirometry, with mean Forced Expiratory Volume in 1 Second (FEV1) and Forced Vital Capacity (FVC) above 90% predicted. However, a significant proportion exhibited reductions in lung volumes, with total lung capacity (TLC) decreasing in 35%, and diffusion capacity (DLCO/TLCO) decreasing in 75%. Lung function pattern analysis showed 88% of patients had normal function, while 12% displayed a restrictive pattern; no obstructive or mixed patterns were observed. Radiographic assessment revealed that 58% of chest X-rays were normal, whereas CT scans showed ground-glass opacities (GGO) in 65% of patients and fibrotic changes in 55%, along with findings such as atelectasis, air trapping, and bronchial wall thickening. Conclusions: Spirometry alone is insufficient to detect impairment of gas exchange or underlying histopathological changes in patients with long COVID. Our findings show that, despite normal spirometry results, many patients exhibit significant diffusion impairment, fibrotic alterations, and ground-glass opacities, indicating persistent lung and microvascular damage. These results underscore the importance of comprehensive assessment using multiple diagnostic tools to identify and manage chronic pulmonary dysfunction in long COVID. Full article

Background/Objectives: Fragility fractures of the pelvis (FFPs) are a significant concern in the elderly population, often leading to severe morbidity and mortality. This study aims to evaluate the diagnostic challenges, clinical outcomes, and mortality rates associated with FFPs in patients referred to multiple hospitals. Methods: A total of 99 patients with suspected pelvic fragility fractures were enrolled between January 2023 and June 2025. Initial diagnoses were made using plain X-rays, with computed tomography (CT) utilized to assess posterior ring fractures. Data on demographics, fracture types according to the Fragility Fracture of the Pelvis (FFP) Classification, hemoglobin levels, and mortality rates were collected and analyzed. Results: The findings revealed that while plain X-rays identified only anterior pelvic ring fractures, CT scans detected posterior ring fractures in 60.6% of cases. Patients with Nakatani II and III pelvic ramus fractures exhibited the most significant decreases in hemoglobin levels. The overall mortality rate was found to be 13.13%, with the highest rates observed in FFP I (13.5%) and FFP II (11.9%) groups. Conclusions: The findings of this study underscore the importance of CT imaging in the diagnosis of FFPs and highlight the need for close monitoring of hemoglobin levels in affected patients. This study also emphasizes the increased mortality risk associated with more complex fracture types. Future research should focus on evaluating functional independence and treatment outcomes to guide clinical decision-making in managing fragility fractures of the pelvis. Full article

Background: Tuberculosis (TB) is a significant global health issue, particularly in resource-limited regions where radiological expertise is constrained. This project aims to develop a scalable deep learning system that safeguards privacy and achieves high accuracy in the early identification of tuberculosis using chest X-ray images. The objective is to implement federated learning with an adaptive-weighted ensemble optimised by a Genetic Algorithm (GA) to address the challenges of centralised training and single-model approaches. Method: We developed an ensemble learning method that combines multiple locally trained models to improve diagnostic consistency and reduce individual-model bias. An optimisation system that autonomously selected the optimal ensemble weights determined each model’s contribution to the final decision. A controlled augmentation process was employed to enhance the model’s robustness and reduce the likelihood of overfitting by introducing realistic alterations to appearance, geometry, and acquisition conditions. Federated learning facilitated collaboration among universities for training while ensuring data privacy was maintained during the establishment of the optimal ensemble at each location. In this system, just model parameters were transmitted, excluding patient photographs. This enabled the secure amalgamation of global data without revealing sensitive clinical information. Standard diagnostic metrics, including accuracy, sensitivity, precision, F1 score, AUC, and confusion matrices, were employed to evaluate the model’s performance. Results: The proposed federated, GA-optimized ensemble demonstrated superior performance compared with individual models and fixed-weight ensembles. The system achieved 98% accuracy, 97% F1 score, and 0.999 AUC, indicating highly reliable discrimination between TB-positive and typical cases. Federated learning preserved model robustness across heterogeneous data sources, while ensuring complete patient privacy. Conclusions: The proposed federated, GA-optimized ensemble achieves highly accurate and robust early tuberculosis detection while preserving patient privacy across distributed clinical sites. This scalable framework demonstrates strong potential for reliable AI-assisted TB screening in resource-limited healthcare settings. Full article

Background/Objectives: Tibial plateau fractures are difficult to detect using X-ray imaging due to limited three-dimensional visibility. This study evaluated the performance of four You Only Look Once (YOLO) deep learning models trained on different radiographic views for fracture detection. Methods: A total of 1489 knee X-rays were collected from a tertiary referral hospital, comprising 727 fracture images and 762 non-fracture images. YOLOv4, YOLOv5, YOLOv8, and YOLOv9 were each trained using anteroposterior (AP), lateral, and combined views. Results: YOLO models trained on AP views consistently outperformed those trained on other views. YOLOv9 trained on AP images achieved the highest accuracy, specificity, precision, F1-score, and area under the curve (AUC) of 0.99, with both sensitivity and negative predictive value (NPV) at 1.00. YOLOv8 trained on AP views reached 0.97 across all metrics with an AUC of 0.98. YOLOv5 trained on AP images achieved an accuracy and F1-score of 0.98, a sensitivity and NPV of 0.97, and an AUC of 1.00. YOLOv4 trained on AP views showed slightly lower performance, with an accuracy and F1-score of 0.96 and an AUC of 1.00. External validation confirmed the strong generalizability of AP-trained models, particularly YOLOv9, which reached an accuracy of 0.87, a sensitivity of 1.00, a specificity of 0.75, a precision of 0.80, an NPV of 1.00, an F1-score of 0.88, and an AUC of 0.93. Artificial intelligence-assisted YOLO models showed strong potential in detecting tibial plateau fractures. Conclusions: Models trained on AP views consistently achieved better diagnostic accuracy than those using other views. Among all, YOLOv9 delivered the best results, highlighting the benefits of newer deep learning architectures. Full article

Artificial Intelligence (AI)-enabled diagnosis has emerged as a promising avenue for revolutionizing medical image analysis, such as X-ray analysis, across a wide range of healthcare disciplines, including dentistry, consequently offering swift, efficient, and accurate solutions for identifying various dental conditions. In this study, we investigated the application of the YOLOv9 model, a cutting-edge object detection algorithm, to automate the diagnosis of dental diseases from X-ray images. The proposed methodology encompasses a comprehensive analysis of dental datasets, as well as preprocessing and model training. Through rigorous experimentation, remarkable accuracy, precision, recall, mAP@50, and an F1-score of 84.89%, 89.2%, 86.9%, 89.2%, and 88%, respectively, are achieved. With significant improvements over the baseline model of 17.9%, 15.8%, 18.5%, and 16.81% in precision, recall, mAP@50, and F1-score, respectively, with 7.9 ms inference time. This demonstrates the effectiveness of the proposed approach in accurately identifying dental conditions. Additionally, we discuss the challenges in automated diagnosis of dental diseases and outline future research directions to address knowledge gaps in this domain. This study contributes to the growing body of literature on AI in dentistry, providing valuable insights for researchers and practitioners. Full article

Background and Objectives: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and compromised bone microarchitecture, leading to an elevated risk of fractures and significant morbidity, particularly among aging populations. Early diagnosis and personalized management are critical to reducing fracture incidence and associated healthcare burdens. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to potential improvements in enhancing osteoporosis care by enabling accurate diagnostic imaging analysis, robust fracture risk prediction, and personalized therapeutic strategies. Materials and Methods: We performed a narrative review to summarize and critically evaluate the current literature on AI and ML applications in osteoporosis diagnosis and management. We searched relevant literature from inception to January 2025 to provide a comprehensive perspective, focusing on key themes, methodological approaches, and clinical implications. Results: Deep learning models, especially convolutional neural networks, facilitate rapid and accurate bone mineral density assessment from routine radiographs, expanding screening capabilities beyond conventional dual-energy X-ray absorptiometry (DXA). Machine learning algorithms harness clinical and demographic data to generate fracture risk models that often outperform traditional tools, enabling timely identification of high-risk individuals. Furthermore, AI-driven analyses of historical treatment responses coupled with real-time monitoring through wearable technologies and mobile applications allow for personalized therapeutic optimization and enhance patient engagement. Despite these promising advances, challenges remain regarding ethical considerations, data privacy, legal liability, incomplete model validation, lack of standardization, and the need for critical appraisal of real-world clinical efficacy for widespread clinical adoption. Conclusions: This narrative review indicates that AI and ML hold significant promise to revolutionize osteoporosis management by enabling early detection, precise risk stratification, and tailored interventions. However, the current evidence is heterogeneous, often lacking robust external validation and quantitative synthesis. Critical gaps include insufficient evaluation of model robustness across diverse populations, discussion of negative or conflicting results, and a comprehensive assessment of the limitations inherent in current AI evidence. Strategic efforts to validate, regulate, and critically integrate these technologies into routine clinical workflows are essential to realize their full potential and address the growing burden of osteoporosis worldwide. Full article

Objective: This study aimed to investigate the relationship between computed tomography (CT)-derived Hounsfield Unit (HU) measurements and dual-energy X-ray absorptiometry (DXA) and to evaluate the feasibility of using contrast-enhanced abdominal CT as a complementary tool in the assessment of bone health in liver transplant recipients. Methods: This retrospective descriptive and analytical study included adult liver transplant recipients who underwent both contrast-enhanced abdominal CT and DXA within a three-month interval. HU measurements were obtained from sagittal and axial reformatted images at the lumbar spine (L1–L4) and femoral neck. All CT examinations were performed using a standardized venous-phase protocol. DXA-derived T-scores from the lumbar spine and femur served as the reference standard. Correlation analyses and receiver operating characteristic (ROC) curves were used to evaluate associations between HU values and BMD, as well as the diagnostic performance of HU in identifying low bone density. Results: A total of 259 recipients (mean age 55.7 ± 14.4 years; 62.9% male) were included. Based on lumbar spine DXA, 17.8% had normal BMD, 36.7% were osteopenic, and 45.5% were osteoporotic. CT-derived HU values at both the lumbar spine and femoral neck were significantly lower in patients with reduced BMD and showed a graded decline across worsening DXA categories. HU values demonstrated positive correlations with corresponding T-scores. Diagnostic performance for detecting osteoporosis was fair, with AUCs of 0.700 (sagittal), 0.698 (axial), and 0.751 (femoral). Conclusion: Contrast-enhanced abdominal CT provides useful ancillary information for opportunistic bone health assessment. CT-derived HU values offer a rapid and cost-effective complementary tool but should not replace DXA in the diagnostic evaluation of osteoporosis Full article

Background/Objectives: Scaphoid fractures are a common wrist injury, typically diagnosed and treated through X-ray imaging, a process that is often time-consuming. Among the various types of scaphoid fractures, occult and nondisplaced fractures pose significant diagnostic challenges due to their subtle appearance and variable bone density, complicating accurate identification via X-ray images. Therefore, creating a reliable assist diagnostic system based on deep learning for the scaphoid fracture detection and localization is critical. Methods: This study proposes a scaphoid fracture detection and localization framework based on diffusion models. In Stage I, we augment the training data set by embedding fracture anomalies. Pseudofracture regions are generated on healthy scaphoid images, producing healthy and fractured data sets, forming a self-supervised learning strategy that avoids the complex and time-consuming manual annotation of medical images. In Stage II, a diffusion-based reconstruction model learns the features of healthy scaphoid images to perform high-quality reconstruction of pseudofractured scaphoid images, generating healthy scaphoid images. In Stage III, a U-Net-like network identifies differences between the target and reconstructed images, using these differences to determine whether the target scaphoid image contains a fracture. Results: After model training, we evaluated its diagnostic performance on real scaphoid images by comparing the model’s results with precise fracture locations further annotated by physicians. The proposed method achieved an image area under the receiver operating characteristic curve (AUROC) of 0.993 for scaphoid fracture detection, corresponding to an accuracy of 0.983, recall of 1.00, and precision of 0.975. For fracture localization, the model achieved a pixel AUROC of 0.978 and a pixel region overlap of 0.921. Conclusions: This study shows promise as a reliable, powerful, and scalable solution for the scaphoid fracture detection and localization. Experimental results demonstrate the strong performance of the denoising diffusion models; these models can significantly reduce diagnostic time while precisely localizing potential fracture regions, identifying conditions overlooked by the human eye. Full article