Search Results (45)

Traumatic liver injury remains a significant contributor to trauma-related morbidity and mortality worldwide. In Saudi Arabia, motor vehicle accidents (MVAs) are the predominant mechanism of injury, particularly among young adults. This study aimed to evaluate the clinical characteristics, management strategies, and outcomes of patients with liver trauma over a ten-year period at a tertiary academic level-one trauma center. A retrospective cohort study was conducted from January 2015 to December 2024. All adult patients (aged 18–65 years) who sustained blunt or penetrating liver injuries and underwent a pan-CT trauma survey were included. Demographic data, Injury Severity Scores (ISSs), imaging timelines, management approach, and clinical outcomes were analyzed. Statistical analysis was performed using JASP software with a significance threshold set at p < 0.05. A total of 111 patients were included, with a mean age of 33 ± 12.4 years; 78.1% were male. MVAs were the leading cause of injury (75.7%). Most patients (80.2%) had low-grade liver injuries and received non-operative management (NOM), with a high NOM success rate of 94.5%. The median time to CT was 55 ± 64 min, and the mean time to operative or IR intervention was 159.9 ± 78.8 min. Complications occurred in 32.4% of patients, with ventilator-associated pneumonia (19.8%) being most common. The overall mortality was 6.3%. Multivariate analysis revealed that shorter time to CT significantly reduced mortality risk (OR = 0.5, p < 0.05), while a positive e-FAST result was strongly associated with increased mortality (OR = 3.3, p < 0.05). Higher ISSs correlated with longer monitored unit stays (ρ = 0.3, p = 0.0014). Traumatic liver injuries in this cohort were predominantly low-grade and effectively managed conservatively, with favorable outcomes. However, delays in imaging and operative intervention were observed, underscoring the requirement for streamlined trauma workflows. These findings highlight the requirement for continuous trauma system improvement, including protocol optimization and timely access to imaging and surgical intervention. Full article

The Extended Focused Assessment with Sonography for Trauma (eFAST) plays a crucial role in the emergency department (ED) by providing rapid and non-invasive diagnostic information in trauma patients. It is a diagnostic-free fluid detection technique that uses sonography to assess trauma in different anatomical windows of the chest and abdomen and has been accepted in multiple studies as the initial diagnostic tool for torso injuries in blunt abdominal trauma. By promptly identifying potentially life-threatening injuries, such as haemoperitoneum, haemothorax, and cardiac tamponade, eFAST facilitates timely intervention and improves patient outcomes in the ED. The eFAST exam is reliable, with high sensitivity and specificity, even when performed by non-radiological personnel, saving time and resources in the chaotic emergency environment. This review aims to assess the diagnostic reliability and limitations of eFAST in different trauma conditions and to outline its goals in trauma critical care and in “ABCDE” resuscitation. Full article

Process-based crop growth models, as an important analytical tool in agricultural production, face the problem of calibrating many parameters during the application process, and sensitivity analysis (SA) can quantify the effects of the model input parameters on the model output and provide an important basis for parameter calibration. In this study, we combined the good performance of the Agricultural Production Systems sIMulator Next-Generation (APSIM NG) model in simulating crop growth, soil carbon and nitrogen cycles, and soil N₂O emissions with the efficient computational efficiency of the extended Fourier amplitude test (EFAST) method. The sensitivity of the APSIM NG model to the simulation of soil N₂O emissions was systematically investigated using the EFAST method in a dry-crop wheat field in the semi-arid region of the Loess Plateau in Longzhong, China, where 28 crop cultivar parameters, 15 soil parameters, 4 meteorological parameters, and 4 field management parameters were selected. The parameters were selected based on the existing literature and the official documents of the model, and the parameter boundaries were determined based on the initial values of the APSIM NG model and the measured data and adjusted upward and downward by the standard normal distribution. In this study, parameters with a first-order sensitivity index ($S_i$) > 0.05 and a total sensitivity index ($S_{Ti}$) > 0.10 were identified as having a significant influence on the model outputs. The results of this study demonstrated that soil N₂O emission modeling in dry-crop wheat fields showed high sensitivity to the following parameters: (1) Among the crop cultivar parameters, the sensitivity from high to low was the leaf appearance rate, maximum leaf area, maximum nitrogen concentration of the grain, and thermal time from the starting grain-fill stage to end grain-fill stage. (2) Among the soil parameters, the sensitivity from high to low was a lower effective moisture limit, wilting coefficient, and ammonium nitrogen content. (3) Among the meteorological parameters, precipitation and solar radiation showed high sensitivity. (4) Among the field management parameters, the nitrogen application rate exhibited the most significant sensitivity. For this reason, we believe that by prioritizing the calibration of the most sensitive parameters through the results of the sensitivity analysis in this study, the workload of the APSIM NG model in the calibration process can be effectively reduced, which is conducive to the rapid localization and application of the model. Full article

Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury. Full article

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies. Full article

The Biome-BGC model is a classic forest ecosystem carbon cycle model driven by remote sensing and plant trait data, and it has been widely applied in various regions of China over the years. However, does the Biome-BGC model have good applicability in all regions of China? This question implies that the rationality of some applications of the Biome-BGC model in China might be questionable. To quantitatively assess the overall spatial applicability of the Biome-BGC model in China’s vegetation ecosystems, this study selected ten representative forest and grassland ecosystem sites, all of which have publicly available carbon flux data. In this study, we first used the EFAST method to identify the sensitive ecophysiological parameters of the Biome-BGC model at these sites. Subsequently, we calibrated the optimal values of these sensitive parameters through a literature review and the PEST method and then used these to drive the Biome-BGC model to simulate the productivity (including GPP and NEP) of these ten forest and grassland ecosystems in China. Finally, we compared the simulation accuracy of the Biome-BGC model at these ten sites in detail and established the spatial pattern of the model’s applicability across China. The results show that the sensitive ecophysiological parameters of the Biome-BGC model vary with spatial distribution, plant functional types, and model output variables. After conducting parameter sensitivity analysis and optimization, the simulation accuracy of the Biome-BGC model can be significantly improved. Additionally, for forest ecosystems in China, the model’s simulation accuracy decreases from north to south, while for grassland ecosystems, the accuracy increases from north to south. This study provides a set of localized ecophysiological parameters and advocates that the use of the Biome-BGC model should be based on parameter sensitivity analysis and optimization. Full article

In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlement and may cause potential safety hazards to the surrounding existing buildings, directly affecting the safety of urban operation. When simulating and predicting surface settlements, the small-strain soil hardening model can more accurately characterize the mechanical parameters of soil. Nevertheless, its parameters are numerous and complicated to determine accurately, so parameter inversion is needed to determine the accurate parameters of the soft soil layer in order to more accurately predict the surface settlement. This study uses the EFAST method to analyse the sensitivity of the HSS model parameters of soft soil strata. It is determined that the parameters that have the most significant impact on the surface settlement are the reference tangent modulus, rebound modulus, and effective cohesion. Then, XGBoost’s fast calculation speed and high precision of SSA inversion are used to inverse and optimize the parameters with high sensitivity. Finally, according to the parameters of the soft soil layer obtained from inversion and measured data, the settlement deformation and safety behaviour of existing buildings are analysed. Combined with the actual shield tunnel project in a city along a river, the inversion calculation shows that the overall average error of the transverse monitoring section is 1.04 mm, and the average maximum error of each monitoring point in the overall shield process is 2.87 mm. The prediction effect is significantly improved compared with the original parameters. The accuracy of the inversion of soil layer parameters is verified from the perspective of time and space. The average settlement of the river embankment foundation is 2.5 mm. Compared with the original parameter data, the prediction results have been greatly improved, and the settlement deformation results are more consistent with the measured data. Full article

Background: Emergency medicine in Romania has developed fast since inception. The need for faster diagnostic capabilities due to the high workload pre- and in-hospital made point-of-care ultrasound (POCUS) a logical next step. The advantages of POCUS are well known, but implementation presents challenges. Our goal was to study how a straightforward method of implementation would work locally. Methods: Two prospective observational studies were conducted at 6 months (prehospital) and 4 months (in-hospital). The protocol used was extended focused assessment sonography in trauma (eFAST), and the shock index (SI) was used to stratify patients. Voluntary sampling was conducted by emergency physicians. The primary outcomes were patient numbers, type of case use, results, and accuracy. Results: The prehospital study registered 34 patients: 41% traumas, 35% cardiac arrest, 18% shock, and 6% acute respiratory distress. The in-hospital study patients were 78: 36% traumas, 6% cardiac arrests, 41% shock, and 17% acute respiratory distress. A total of 88.5% of the cases were confirmed with definitive imagistic findings. Conclusion: The studies mark an increase in POCUS usage and use in complicated cases. Providing supervision and feedback into clinical practice resulted in a further increase in POCUS usage, the second study having an 88.5% accuracy when compared to the final diagnostic proving the increased efficiency of a longitudinal training approach. Full article

Parameters such as the leaf area index (LAI), canopy chlorophyll content (CCH), and canopy carotenoid content (CCA) are important indicators for evaluating the ecological functions of forests. Currently, rapidly developing unmanned aerial vehicles (UAV) equipped with hyperspectral technology provide advanced technical means for the real-time dynamic acquisition of regional vegetation canopy parameters. In this study, a hyperspectral sensor mounted on a UAV was used to acquire the data in the study area, and the canopy parameter estimation model of moso bamboo forests (MBF) was developed by combining the PROSAIL radiative transfer model and the machine learning regression algorithm (MLRA), inverted the canopy parameters such as LAI, CCH, and CCA. The method first utilized the extended Fourier amplitude sensitivity test (EFAST) method to optimize the global sensitivity analysis and parameters of the PROSAIL model, and the successive projections algorithm (SPA) was used to screen the characteristic wavebands for the inversion of MBF canopy parameter inversion. Then, the optimized PROSAIL model was used to construct the ‘LAI-CCH-CCA-canopy reflectance’ simulation dataset for the MBF; multilayer perceptron regressor (MLPR), extra tree regressor (ETR), and extreme gradient boosting regressor (XGBR) employed used to construct PROSAIL_MLPR, PROSAIL_ETR, and PROSAIL_XGBR, respectively, as the three hybrid models. Finally, the best hybrid model was selected and used to invert the spatial distribution of the MBF canopy parameters. The following results were obtained: Waveband sensitivity analysis reveals 400–490 and 710–1000 nm as critical for LAI, 540–650 nm for chlorophyll, and 490–540 nm for carotenoids. SPA narrows down the feature bands to 43 for LAI, 19 for CCH, and 9 for CCA. The three constructed hybrid models were able to achieve high-precision inversion of the three parameters of the MBF, the model fitting accuracy of PROSAIL_MLRA reached more than 95%, with lower RMSE values, and the PROSAIL_XGBR model yielded the best fitting results. Our study provides a novel method for the inversion of forest canopy parameters based on UAV hyperspectral data. Full article

Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3’s temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3’s resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale. Full article

Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting. Full article

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and R_act25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems. Full article

Background: Dyspnea commonly stems from combined myocardial and pulmonary dysfunction, posing challenges for accurate pre-hospital diagnosis. Limited diagnostic capabilities hinder the differentiation of cardiac and pulmonary issues. This study assesses the efficacy of combined cardiac and pulmonary ultrasound using the BLUE, eFAST, and FATE protocols. Methods: Participants were consecutively enrolled from dyspnea-related emergency calls in Warsaw, Poland, from 4 April 2022, to 15 June 2023. Patients with pleural effusion were identified through pre-hospital and in-hospital radiological assessments. Pre-hospital thoracic ultrasonography followed the BLUE, eFAST, and FATE protocols, alongside comprehensive clinical assessments. The pre-hospital diagnoses were juxtaposed with the with hospital discharge diagnoses. Results: Sixteen patients (8 men, 8 women; median age: 76 years) were enrolled. Inter-rater agreement for the BLUE protocol was substantial (κ = 0.78), as was agreement for eFAST (κ = 0.75), with almost perfect agreement for combined protocol assessment (κ = 0.83). Left ventricle hypokinesis, identified via the FATE protocol, significantly correlated with hospital-diagnosed decompensated heart failure as the primary cause of dyspnea. Sensitivity and specificity were 1.0 (95%CI: 0.62–1.0) and 0.6 (95%CI: 0.15–0.95), respectively. Positive predictive value was 0.85 (95%CI: 0.55–0.98), and diagnostic accuracy was 0.86 (95%CI: 0.62–0.98). Conclusions: Integrating the FATE protocol into BLUE and eFAST enhances pre-hospital differential diagnosis accuracy of pleural effusion in adults. This synergistic approach streamlines diagnostic processes and facilitates informed clinical decision-making. Larger-scale validation studies are needed for broader applicability. Full article

Point-of-care ultrasound (PoCUS) has become an indispensable standard in emergency medicine. Emergency medicine ultrasound (EMUS) is the application of bedside PoCUS by the attending emergency physician to assist in the diagnosis and management of many time-sensitive health emergencies. In many ways, using PoCUS is not only the mere application of technology, but also a fusion of already existing examiner skills and technology in the context of a patient encounter. EMUS practice can be defined using distinct anatomy-based applications. The type of applications and their complexity usually depend on local needs and resources, and practice patterns can vary significantly among regions, countries, or even continents. A different approach suggests defining EMUS in categories such as resuscitative, diagnostic, procedural guidance, symptom- or sign-based, and therapeutic. Because EMUS is practiced in a constantly evolving emergency medical setting where no two patient encounters are identical, the concept of EMUS should also be practiced in a fluid, constantly adapting manner driven by the physician treating the patient. Many recent advances in ultrasound technology have received little or no attention from the EMUS community, and several important technical advances and research findings have not been translated into routine clinical practice. The authors believe that four main areas have great potential for the future growth and development of EMUS and are worth integrating: 1. In recent years, many articles have been published on novel ultrasound applications. Only a small percentage has found its way into routine use. We will discuss two important examples: trauma ultrasound that goes beyond e-FAST and EMUS lung ultrasound for suspected pulmonary embolism. 2. The more ultrasound equipment becomes financially affordable; the more ultrasound should be incorporated into the physical examination. This merging and possibly even replacement of aspects of the classical physical exam by technology will likely outperform the isolated use of stethoscope, percussion, and auscultation. 3. The knowledge of pathophysiological processes in acute illness and ultrasound findings should be merged in clinical practice. The translation of this knowledge into practical concepts will allow us to better manage many presentations, such as hypotension or the dyspnea of unclear etiology. 4. Technical innovations such as elastography; CEUS; highly sensitive color Doppler such as M-flow, vector flow, or other novel technology; artificial intelligence; cloud-based POCUS functions; and augmented reality devices such as smart glasses should become standard in emergencies over time. Full article

Lung transthoracic ultrasound (LUS) is an accessible and widely applicable method of rapidly imaging certain pathologies in the thorax. LUS proves to be an optimal tool in respiratory emergency medicine, applicable in various clinical settings. However, despite the rapid development of bedside ultrasonography, or point-of-care (POCUS) ultrasound, there remains a scarcity of knowledge about the use of LUS in pre-hospital settings. Therefore, our aim was to assess the usefulness of LUS as an additional tool in diagnosing dyspnea when performed by experienced paramedics in real-life, pre-hospital settings. Participants were recruited consecutively among patients who called for an emergency due to dyspnea in the Warsaw region of Poland. All the enrolled patients were admitted to the Emergency Department (ED). In the prehospital setting, a paramedic experienced in LUS conducted an ultrasonographic examination of the thorax, including Bedside Lung Ultrasound in Emergency (BLUE) and extended Focused Assessment with Sonography for Trauma (eFAST) protocols. The paramedic’s diagnosis was compared to the ED diagnosis, and if available, to the final diagnosis established on the day of discharge from the hospital. We enrolled 44 patients in the study, comprising 22 (50%) men and (50%) women with a median age of 76 (IQR: 69.75–84.5) years. The LUS performed by paramedic was concordant with the discharge diagnosis in 90.91% of cases, where the final diagnosis was established on the day of discharge from the hospital. In cases where the patient was treated only in the ED, the pre-hospital LUS was concordant with the ED diagnosis in 88.64% of cases. The mean time of the LUS examination was 63.66 s (SD: 19.22). The inter-rater agreement between the pre-hospital diagnosis and ER diagnosis based on pre-hospital LUS and complete ER evaluation was estimated at k = 0.822 (SE: 0.07; 95%CI: 0.68, 0.96), indicating strong agreement, and between the pre-hospital diagnosis based on LUS and final discharge diagnosis, it was estimated at k = 0.934 (SE: 0.03; 95%CI: 0.88, 0.99), indicating almost perfect agreement. In conclusion, paramedic-acquired LUS seems to be a useful tool in the pre-hospital differential diagnosis of dyspnea in adults. Full article