Identification Of Fractures Research Articles

GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops

GeoCrack is the first large-scale open source annotated dataset of fracture traces from geological outcrops, enabling deep learning-based fracture segmentation, setting a new standard for natural fracture characterization datasets. GeoCrack contains images from photogrammetric surveys of fractured rock exposures across 11 sites in Europe and the Middle East, capturing diverse lithologies and tectonic settings. Each image was cleaned, normalized, and manually segmented, followed by a recursive annotation vetting process to ensure the quality and accuracy of the digitized fracture edges. The processed images and corresponding binary masks were divided into 224 × 224 patches, yielding 12,158 pairs. GeoCrack captures representive real-world challenges in fracture edge annotation, such as contrast variations between fracture traces and the host medium due to geological and geomorphological factors like aperture dilation, host rock composition, outcrop weathering, and groundwater staining. Physical occlusions like shadows and vegetation are also considered to minimize false positives. GeoCrack was validated using a U-Net implementation for fracture segmentation, achieving satisfactory IoU of 85%. GeoCrack holds strong potential to advance deep fracture segmentation in geological applications, effectively tackling the diverse challenges of real-world fracture identification.

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Vertebral Fracture Prediction from MRI-based Vertebral Bone Quality Scores in Postmenopausal Women

Study Design. A longitudinal cohort study Objective. The study aims to assess the lumbar MRI-based vertebral bone quality (VBQ) score and dual energy x-ray absorptiometry (DXA) T score in a longitudinal cohort of postmenopausal women and to evaluate the performance of these scores in predicting VF over 5 years of follow-up. Summary of Background Data. VBQ scores can be used for osteoporosis screening and identification of vertebral fractures (VFs). However, due to limitations in prior cross-sectional studies, definitive conclusions about the causal associations between the VBQ score and incident VF have not been drawn. Methods. Postmenopausal women with baseline DXA and lumbar MRI data were enrolled. Follow-up assessments were conducted over 5 years after the baseline examination; both anteroposterior- and lateral-view thoracolumbar spine radiographs were captured to evaluate incident VF. At baseline, correlation analyses between the VBQ score and osteoporosis or preexisting VF were performed. A Cox regression model for multivariate logistic regression was used to analyze the risk factors for incident VF. The hazard ratio (HR) was calculated. Results. 137 postmenopausal women were included. The VBQ score was moderately correlated with lumbar bone mineral density at baseline (r=0.4, P<0.001). Patients with incident VF had significantly lower T scores (-3.4 vs. -1.87, P<0.001) and higher VBQ scores (4.62 vs. 4.03, P<0.001) at baseline than those without incident VF. After adjusting for age, weight, creatinine clearance, and alkaline phosphatase levels, the HRs for the VBQ score and T score were 1.93 and 0.71, respectively. After adjusting for the T score, an increased VBQ score was independently associated with an increased risk of incident VF, with an HR of 1.87. Conclusion. The VBQ score was moderately correlated with DXA T score and was a predictor of incident VF risk independent of bone mineral density in postmenopausal women.

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model's performance on the external dataset showed similar accuracy levels. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures.

Unbalanced graph isomorphism network for fracture identification by well logs

Unbalanced graph isomorphism network for fracture identification by well logs

Quantitative ROI differences for assessment of occult intertrochanteric extension of greater trochanteric fractures on pelvis CT.

To determine if difference in Hounsfield Units (HU) of the medullary bone between the injured and non-injured femurs in patients with greater trochanteric fractures is associated with occult intertrochanteric (IT) extension. Retrospective review was performed of 81 patients (age range 54-102, 54 females and 27 males) who underwent CT and subsequent MRI after identification of a greater trochanteric fracture without evidence of IT extension on radiography and/or CT. Hounsfield units of the injured and non-injured femurs on CT were recorded at the level of the base of the greater trochanter centrally (ROI1) and the level of the upper border of the lesser trochanter posteromedially (ROI2). The difference between the injured and non-injured femur for each ROI1 and ROI2 were calculated. Absence or presence, and if present, extent of IT extension was assessed on MRI. Analysis was performed to determine if there is correlation of difference in density with presence, and extent of occult IT fractures. 81 cases met inclusion criteria, 14 (17%) had no IT extension, 11 (14%) had less than 50% IT extension and 56 (69%) had 50% or greater IT extension. There was statistical significance between presence and absence of IT extension between the injured and non-injured femur for ROI1 (HU) no IT extension median (IQR): 18.8(4-40), ROI1 (HU) present IT extension median (IQR): 65.5(46-90) p < .0001, and for ROI2 no IT extension median (IQR): 3(-8-25.5) and ROI2 present IT extension 51(40.5-76), p < .0001. There was statistical significance of the extent of IT extension: ROI1 less than 50% IT extension median (IQR): 37.5(27.5-57), ROI1 50% or greater IT extension median (IQR): 72.3(53.5-91.3), p < .0001, and for ROI2 less than 50% IT extension median (IQR): 17.5(8-49), and ROI2 50% or greater IT extension median (IQR): 55.8(45.3-81.5), p < .0001. A threshold ROI2 difference of 50 HU resulted in specificity of 92% and sensitivity 60.7% of for IT extension 50% or greater. Patients presenting with greater trochanteric fractures, an asymmetric increased density measured by ROI differences within the medullary space of the IT region between the injured and non-injured femur is associated with occult IT extension.

Toward the Application of Dual-Energy Computed Tomography with Virtual Non-Hydroxyapatite Color-Coded Maps to Identify Traumatic Fractures in Daily Emergency Settings.

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and <2 years of experience in musculoskeletal imaging. Both techniques were evaluated in terms of sensitivity (SE), specificity (SP), positive and negative predictive values (PPVs and NPVs) and accuracy for fracture detection, as confirmed at a 3-month clinical-instrumental follow-up. Inter-observer agreement and examination times were also analyzed. Fractures were confirmed in 18/40 cases. The highest values of diagnostic performance for VNHAP images were obtained in terms of SP (90.9% and 95%) and PPV (87.5% and 92.8%) and for the less experienced operator. No statistically significant differences were observed between the diagnostic accuracy of the two readers in the evaluation of VNHAP images. Inter-observer agreement was moderate (κ = 0.536) for BCT and substantial (κ = 0.680) for VNHAP. Comparing the two operators, a significantly longer examination time for BCT and no significant difference for VNHAP were registered. Our preliminary experience may encourage the employment of VNHAP maps in combination with BCT images in emergency settings. Their use could be time-saving and valuable in terms of diagnostic performance, especially for less experienced operators.

Multiscale Characterization of Fractures and Analysis of Key Controlling Factors for Fracture Development in Tight Sandstone Reservoirs of the Yanchang Formation, SW Ordos Basin, China

Tight sandstone reservoirs, despite their low porosity and permeability, present considerable exploration potential as unconventional hydrocarbon resources. Natural fractures play a crucial role in hydrocarbon migration, accumulation, and present engineering challenges such as late-stage reformation in these reservoirs. This study examines fractures in the seventh member of the Triassic Yanchang Formation’s tight sandstone within the Ordos Basin using a range of methods, including field outcrops, core samples, imaging and conventional logging, thin sections, and scanning electron microscopy. The study clarifies the characteristics of fracture development and evaluates the relationship between dynamic and static rock mechanics parameters, including the calculation of the brittleness index. Primary factors influencing fracture development were quantitatively assessed through a combination of outcrop, core, and mechanical test data. Findings reveal that high-angle structural fractures are predominant, with some bedding and diagenetic fractures also present. Acoustic, spontaneous potential, and caliper logging, in conjunction with imaging data, enabled the development of a comprehensive probabilistic index for fracture identification, which produced favorable results. The analysis identifies four key factors influencing fracture development: stratum thickness, brittleness index, lithology, and rock mechanical stratigraphy. Among these factors, stratum thickness is negatively correlated with fracture development. Conversely, the brittleness index positively correlates with fracture development and significantly influences fracture length, aperture, and linear density. Fractures are most prevalent in siltstone and fine sandstone, with minimal development in mudstone. Different rock mechanics layer types also impact fracture development. These insights into fracture characteristics and controlling factors are anticipated to enhance exploration efforts and contribute to the study of similar unconventional reservoirs.

3D-printed model is a useful addition in orthopedic resident education for the understanding of tibial plateau fractures

This study aimed to explore the role of the three-dimension (3D) printed models in orthopedic resident training of tibial plateau fractures. A total of 41 residents from our institution were divided into two groups. The intervention group, consisting of 20 residents, had access to 3D-printed models illustrating thirteen tibial plateau fractures. In contrast, the control group, comprising 21 residents, received digital images of thirteen identical tibial plateau fractures. Evaluation of learning outcomes included the accurate identification of tibial plateau fracture patterns, deduction of traumatic mechanisms, preoperative plan, assessment time, and subjective questionnaire responses. The participants with 3D printed models scored significantly higher in both the Schatzker classification and Luo three-column classification compared to those without 3D printed models. Residents in the intervention group performed better in accuracy in deducing traumatic mechanisms compared to the control group. In addition, the sum score of preoperative plan in the intervention group was significantly higher than that in the control group. Specifically, participants with 3D printed models scored higher in surgical approach choice and implants placement than these in the control group. Residents exposed to 3D printed models also spent less time to complete the assessment than those with access only to digital imaging. Subjective assessments indicated that 3D-printed models boosted confidence in fracture identification, improved preoperative plan for fracture management and enhanced the understanding in injury mechanism of tibial plateau fractures. Furthermore, residents agreed that the use of 3D-printed models heightened their interest in learning tibial plateau fractures. Therefore, the addition of 3D printed models significantly contributed to a comprehensive understanding of tibial plateau fractures, the improvement in fracture identification, inferring injury mechanisms and preoperative plan.

Feasibility study in quantitative identification of fresh and old osteoporotic vertebral compression fracture by vertebral CT value and CT value difference

BackgroundThis study aims to assess the diagnostic utility of vertebral CT value and CT value difference in distinguishing between fresh and old osteoporotic vertebral compression fractures (OVCF).MethodsA retrospective analysis was conducted on 118 patients with OVCF who underwent both MRI and CT examinations at our hospital. The nature of the fractured vertebra was determined according to MRI. The CT value of the fractured vertebrae and the mean CT value of the adjacent normal vertebrae were measured separately, and the differences between these values were calculated. Independent samples t-tests were used to compare CT value and CT value difference among all groups. The receiver operating characteristic (ROC) curve was employed to determine the optimal cut-off value for both CT value and CT value difference in differentiating fresh and old fractures.ResultsThe study included a total of 163 fractured vertebrae from 118 patients. The CT value of fresh fractured vertebrae was significantly higher than those of adjacent normal vertebrae, which was statistically different (P < 0.001). In contrast, the difference between CT value of old fractured vertebrae and those of adjacent normal vertebrae was not statistically significant (P > 0.05). There were significant differences in CT value and CT value difference between fresh fractured vertebrae and old fractured vertebrae (P < 0.001). The ROC curve analysis showed that the optimal cut-off value of CT value for fresh fractures and old fractures was 103.40 HU. The optimal cut-off value of CT value difference was 39.81 HU.ConclusionVertebral CT value and CT value difference offer a certain reference value for distinguishing between fresh and old OVCF. These parameters can serve as a rapid diagnostic tool when MRI is unavailable or impractical, aiding in the timely assessment of OVCF.

Research on the Recognition and Characterization of Fractures in Coal Rock Based on CT Scanning

Abstract Coal rock fractures play a vital role as key channels for gas migration and storage in the exploration and development of coalbed methane. The characteristics of these fractures, such as porosity distribution, specific surface area, and connectivity, directly impact the adsorption, diffusion, and permeability behaviors of coal reservoirs. Therefore, a detailed analysis of coal rock fracture characteristics is vital in assessing the extractability of coalbed methane and during its exploration and development process. Currently, techniques like imaging logging, seismic detection, and micro-CT are employed for characterizing and studying fractures at various scales. Among these, high-resolution non-destructive CT scanning technology, by constructing three-dimensional data models of core samples, enables a quantitative analysis of fracture distribution features and size parameters. Traditional digital image processing methods, such as threshold segmentation and binary segmentation, though advantageous in computational speed, have limitations in the accuracy of fracture identification. With the advancement of artificial intelligence technology, deep learning-based image processing techniques have increasingly become significant tools for fracture detection due to their powerful feature extraction capabilities. This study conducted micrometer-level CT scanning experiments on coal rock samples, using CT image processing technology to construct a digitalized coal rock core model. Deep learning methods were applied for precise fracture identification, establishing an appropriate deep learning architecture and optimized parameters for coal rock fracture recognition. Furthermore, this paper provides a detailed quantitative characterization of the precisely identified fractures, offering robust technical support for the exploration and development of coalbed methane. The high-resolution non-destructive CT scanning technology and three-dimensional fracture network reconstruction method used in this study have been proven to be effective tools for investigating the micro-features of rocks, enabling the three-dimensional visualization and detailed characterization of rock microstructures.

Incidence of Ipsilateral Femoral Neck and Shaft Fractures in Pediatric and Adolescent Patients

OBJECTIVES: To identify the incidence, patient characteristics, and effectiveness of radiographic screening methods for detecting ipsilateral femoral neck and shaft fractures in pediatric and adolescent trauma patients. METHODS: Design: Retrospective cohort study. Setting: This study was conducted at a tertiary pediatric trauma hospital. Patient Selection Criteria: Patients younger than 18 years treated for a femoral shaft fracture between 2004 and 2018 were reviewed. Pathologic (metabolic bone disease or bone lesion), periprosthetic, and penetrating traumatic femoral shaft fractures were excluded. Outcome Measurements and Comparisons: Patient demographics, mechanisms of injury, treatment methods, and associated injuries were analyzed. Pretreatment x-rays and computed tomography (CT) scans were reviewed for the identification of ipsilateral femoral neck and shaft fractures. RESULTS: Among the 840 pediatric patients included in this study, 4 patients (0.5%) sustained ipsilateral femoral neck and shaft fractures. All the femoral neck fractures were observed in adolescents (aged 13–17 years) and involved in high-energy traumas. In adolescents involved in high-energy trauma, the incidence increased to 1.7%. Pretreatment sensitivity of both x-rays and CT scans was only 50% for the detection of femoral neck fractures. CONCLUSIONS: This study reveals that ipsilateral femoral neck and shaft fractures in pediatric patients are rare, occurring in adolescents involved in high-energy trauma. The findings suggest the need for a selective, rather than routine, use of CT scans based on the patient's age and the mechanism of injury. The use of alternative imaging methods such as magnetic resonance imaging should be considered to balance diagnostic accuracy while minimizing radiation exposure. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Multi-task learning for calcaneus fracture diagnosis of X-ray images

Fracture diagnosis is critical in clinical settings, and imaging modalities like X-ray and CT scans are crucial for bone fracture diagnosis. Although X-ray scans are more convenient and affordable, they are prone to misdiagnosis and missed diagnosis due to the limited number of poses that can be observed and the uneven distribution of grayscales. Deep learning methods show great potential in improving the efficiency and accuracy of x-ray fracture diagnosis.This paper proposes MTL-DlinkNet, a multi-task learning model based on D-linkNet, that can perform both classification tasks for automatic identification of calcaneus fractures, and segmentation tasks for generation of regions of interest (RoI). The model achieves high accuracy (0.989 AUC) while annotating the data at the same time, making diagnosis more efficient and reducing the burden on doctors. The paper also presents an intelligent diagnostic tool for calcaneus fracture that assists doctors in clinical settings. The main contributions of this work include building a dataset of foot X-rays for fracture diagnosis, proposing a better-performing fracture diagnosis deep learning model, and developing an efficient diagnostic tool. Experimental results show that the MTL-DlinkNet model achieves better performance than the baseline models.Overall, this paper demonstrates the potential of deep learning methods in improving fracture diagnosis accuracy and efficiency, especially when multi-task learning is used, with practical applications in clinical settings.

The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs

PurposeTo assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment. Materials and methodsThis retrospective study used a consecutive cohort of 596 chest radiographs from four US hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related International Classification of Diseases, 10th revision diagnostic codes and medication use for the study period and an additional year of follow-up. ResultsThe model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% confidence interval [CI]: 0.939-0.968), sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity 89.2% (95% CI: 85.4%-92.3%). Out of the 236 true-positive cases (ie, correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease-modifying medication for osteoporosis. ConclusionThe model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity of 89.2% (95% CI: 85.4%-92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease-modifying medications.

Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

Fracture failure analysis of heavy-haul train couplers using convolutional neural network along with multilayer perceptron

Abstract Metal couplers are susceptible to unpredictable failure and fracture under long-term high-load conditions in heavy-haul railway transportation. The current mainstream manual inspection method has the disadvantages of high subjectivity and high a priori knowledge requirements, thus not meeting the rapid analysis requirements of production companies. Therefore, in this study, an automated failure analysis method is proposed for heavy-haul coupler fractures. First, a novel image segmentation method (PermuteNet) combining a visual multilayer perceptron and a convolutional neural network is designed to segment different failure patterns of fracture surfaces. The proposed method uses two newly proposed modules—permute attention module and context attention module—to improve the network’s ability to perceive weakly differentiated objects, thereby improving the recognition ability of the model for different failure patterns. In addition, a deep supervisory function is adopted to accelerate the convergence speed of the network. Finally, the proposed image segmentation method is deployed on a computer in conjunction with a developed client application to implement a single-click detection function for coupler fracture pattern analysis. Experiments are performed using the heavy-haul coupler fracture dataset established using on-site data; the proposed segmentation method achieves a mean intersection over union of 77.8%, which is considerably higher than that of other existing methods. By using the client software, the single-click detection function of the fracture area is realized. Thus, the proposed method provides a more convenient and accurate fracture identification solution for factory inspectors and has broad application prospects.

Approach to the Pediatric Patient with Glucocorticoid-Induced Osteoporosis.

Glucocorticoid (GC) therapy remains the cornerstone of treatment for many conditions of childhood and an important cause of skeletal and endocrine morbidity. Here, we discuss cases that bring to life the most important concepts in the management of pediatric GC-induced osteoporosis (pGIO). Given the wide variety of underlying conditions linked to pGIO, we focus on the fundamental clinical-biological principles that provide a blueprint for management in any clinical context. In so doing, we underscore the importance of longitudinal vertebral fracture phenotyping, how knowledge about the timing and risk of fractures influences monitoring, the role of bone mineral density in pGIO assessments, and the impact of growth-mediated "vertebral body reshaping" after spine fractures on the therapeutic approach. Overall, pGIO management is predicated upon early identification of fractures (including vertebral) in those at risk, and timely intervention when there is limited potential for spontaneous recovery. Even a single, low-trauma long bone or vertebral fracture can signal an osteoporotic event in an at-risk child. The most widely used treatments for pediatric osteoporosis, intravenous bisphosphonates, are currently recommended first-line for the treatment of pGIO. It is recognized, however, that even early identification of bone fragility, combined with timely introduction of the most potent bisphosphonate therapies, may not completely prevent osteoporosis progression in all contexts. Therefore, prevention of first-ever fractures in the highest-risk settings is on the horizon, where there is also a need to move beyond anti-resorptives to the study of anabolic agents.

Detection and localization of wrist fractures in x-ray imagery using deep learning teaching

Hospitals often refer patients with wrist fractures, particularly to their emergency rooms. To accurately diagnose these illnesses and choose the appropriate course of therapy, doctors must evaluate images from various medical equipment, medical data, and a physical assessment of the patient. This project attempts to use deep learning on these images to identify wrist X-ray fractures and help physicians diagnose them, particularly in emergency departments. This study employs a dataset comprising both fractured and regular wrists to assess the extent to which the Recurrent Neural Network with 22 Convolutional Neural Network layers (RN22-CNNs) transfers knowledge for fracture identification and classification. We evaluate the diagnostic accuracy of the RN-21CNN model against four popular transfer learning models, namely Visual Geometry Group (Vgg16), ResNet-50, Inception V3, and Vgg19. We used the model on a dataset of 1644 X-rays collected from the Kaggle repository. Next, we trained, verified, and tested the adapted (RN22-CNNs) model. The proposed model had an accuracy of 96.61%. The proposed Computer-Aided Diagnosis System (CADS) will save medical practitioners' burden by accurately identifying fractures.

Identification and parameter characterization of pores and fractures in shales based on multi-scale digital core data

Identification and parameter characterization of pores and fractures in shales based on multi-scale digital core data

Goal-directed osteoporosis treatment: ASBMR/BHOF task force position statement 2024.

The overarching goal of osteoporosis management is to prevent fractures. A goal-directed approach to long-term management of fracture risk helps ensure that the most appropriate initial treatment and treatment sequence is selected for individual patients. Goal-directed treatment decisions require assessment of clinical fracture history, vertebral fracture identification (using vertebral imaging as appropriate), measurement of bone mineral density (BMD), and consideration of other major clinical risk factors. Treatment targets should be tailored to each patient's individual risk profile and based on the specific indication for beginning treatment, including recency, site, number and severity of prior fractures, and BMD levels at the total hip, femoral neck, and lumbar spine. Instead of first-line bisphosphonate treatment for all patients, selection of initial treatment should focus on reducing fracture risk rapidly for patients at very high and imminent risk, such as in those with recent fractures. Initial treatment selection should also consider the probability that a BMD treatment target can be attained within a reasonable period of time and the differential magnitude of fracture risk reduction and BMD impact with osteoanabolic versus antiresorptive therapy. This position statement of the ASBMR/BHOF Task Force on Goal-Directed Osteoporosis Treatment provides an overall summary of the major clinical recommendations about treatment targets and strategies to achieve those targets based on the best evidence available, derived primarily from studies in older postmenopausal women of European ancestry.