Real-time Strain Research Articles

Real-Time Strain Field Measurement Based on Dense Fiber Bragg Gratings Array

Real-Time Strain Field Measurement Based on Dense Fiber Bragg Gratings Array

Research on fatigue life of carbon fiber reinforced polymer strengthened single-sided butt welded joints utilizing resin pre-coating for strong adhesive bonding

Single-sided butt welding is a common connection method used in box beam structures such as excavator booms. Single-sided butt weld joints are prone to fatigue failure since the structures are subjected to cyclic loads over extended periods. The purpose of this study is to improve the fatigue performance of single-sided butt welded joints. Carbon fiber reinforced polymer (CFRP) strengthened single-sided butt welded joint specimens with permanent backing plates were prepared by pre-coating method, and constant-amplitude tension–tension fatigue tests were carried out on the specimens. The effects of welding groove parameters and CFRP reinforcement on fatigue life were studied. The test results show that the CFRP-strengthened specimens without blunt edges and with a groove angle of 40° show the highest average fatigue life. The single-sided bonding of 10 layers of CFRP sheets effectively inhibits the propagation of fatigue cracks and significantly improves the fatigue life of welded joints. In the CFRP/welded joint composite structure, the strain monitoring results show that the peak strain of the interface is the largest in the middle position, and the peak strain rises sharply in the rapid crack propagation stage, which indicates that the real-time interface strain peak can reflect the fatigue crack propagation. It provides an early warning method for real-time monitoring of fatigue life failure of welded structures in practical engineering.

Damage sensing in multi-functional nanocomposite polymer bonded energetics with embedded multi-walled carbon nanotube sensing networks

Abstract This experimental investigation evaluates the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer-bonded energetics (PBEs). PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to maintain the composite’s shape and form. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, the presence of conductive/non-conductive particulate phases, the high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In this study, ammonium perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network’s SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS materials are first evaluated to study the binder’s electromechanical response, followed by AP/MWCNT/PDMS to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating to the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions and mechanical failure modes are analyzed via scanning electron microscopy imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as SHM sensors capable of real-time strain and damage assessment of PBEs.

Statistical analysis of the ground deformation of Vulcanian explosions at Sakurajima volcano, Japan

The forecast of pulsatory explosions during volcanic unrest periods is an essential issue for the assessment and mitigation of volcanic hazards. Although various precursors are detectable through geophysical and geochemical monitoring, difficulties remain in precisely constraining possible scenarios. A probabilistic approach is effective in assessing risk while considering various uncertainties. Sakurajima volcano characterized by frequent Vulcanian activity is one of the suitable fields for the probabilistic forecast of pulsatory explosions. Their inflation-deflation patterns of ground deformation related to Vulcanian explosions are useful for evaluating the imminence and size of the next event. The large database obtained from its vigorous activity can contribute to statistical analysis. In this study, aiming the probabilistic forecast of the timing and size of explosions, we investigated the duration of inflation and volume changes at the pressure source using strain records of over 5000 events of Sakurajima volcano. Then, a stochastic model was estimated to explain the distribution of these events. The log-logistic distribution was found to be an appropriate model for data distribution, indicating the presence of competing processes, such as pressurization and depressurization, in the conduit. The model parameters of the log-logistic distribution temporally fluctuated reflecting the volcanic activity, especially increasing the magma supply from a deep region. We also suggested a methodology to constrain the probabilities of the likely timing and size of an imminent explosion using real-time strain monitoring and an estimated model distribution. Although some improvements would be needed for practical forecasting, our approach could be useful in predicting possible ash hazards.

The Application Value of Using Semiquantitative and Quantitative Parameters in Multimodal Ultrasound to Distinguish Between Benign and Malignant BI-RADS 4 Lesions.

This study aims to explore the value of real-time strain elastography (RTE) and contrast-enhanced ultrasonography (CEUS) in the diagnosis of breast BI-RADS 4 lesions. It collected 85 cases (totaling 85 lesions) diagnosed with breast BI-RADS 4 through routine ultrasound from October 2020 to December 2022 in Huangshan City People's Hospital. All lesions underwent RTE and CEUS examination before surgery, and the ImageJ software was used to measure the periphery of lesion images in the enhancement peak mode and grayscale mode to calculate the contrast-enhanced ultrasound area ratio. The diagnostic capabilities of single-modal and multimodal ultrasound examination for the malignancy of breast BI-RADS 4 lesions were compared using the receiver operating characteristic curve; the Spearman correlation analysis was adopted to evaluate the correlation between multimodal ultrasound and CEUS area ratio. As a result, among the 85 lesions, 51 were benign, and 34 were malignant. The areas under the curve (AUCs) of routine ultrasound (US), US + RTE, US + CEUS, and US + RTE + CEUS were 0.816, 0.928, 0.953, and 0.967, respectively, with the combined method showing a higher AUC than the single application. The AUC of the CEUS area ratio diagnosing breast lesions was 0.888. There was a strong positive correlation (r = 0.819, P < 0.001) between the diagnostic performance of US + RTE + CEUS and the CEUS area ratio. In conclusion, based on routine ultrasound, the combination of RTE and CEUS can further improve the differential diagnosis of benign and malignant lesions in breast BI-RADS 4.

StrainNet-LD: Large Displacement digital image correlation based on deep learning and displacement-field decomposition

First, a displacement-field decomposition strategy is proposed that effectively enhances the accuracy of digital image correlation (DIC) algorithms under large displacement or deformation conditions. The implementation methodologies of displacement-field decomposition under both Lagrangian and Eulerian descriptions are derived and presented. Second, displacement-field decomposition is applied to speckle image correlation algorithms based on deep learning, resolving the contradiction between registration capability of large displacement and the accuracy. A CUDA-accelerated and residual learning-based strain fitting algorithm is proposed for high-precision and real-time strain computation. This method is named “StrainNet for Large Displacement” (StrainNet-LD). StrainNet-LD achieves a calculation speed of 768 × 768 × 6 fps (768 × 768 × 3 fps with strain calculated simultaneously) on GPU with displacement MAE < 0.05 pixels and strain MAE < 0.002ε. Finally, rubber tensile tests, 3D motion measurement of composite shells, and morphology reconstruction experiments of speckle-free composite repair structures are conducted to validate the algorithm's robustness and efficiency. Some vital discussions of displacement-field decomposition are given in the Appendixes. The code and dataset are available at https://github.com/GW-Wang-thu/StrainNet-LD.

Value of elastography in characterization of solid renal masses

BackgroundSolid renal masses often come to light as incidental findings during abdominal ultrasound examinations. Once detected, determining whether a renal mass is benign or malignant becomes imperative for informed decision-making regarding management and treatment strategies. In this investigation, the aim was to explore the diagnostic efficacy of real-time strain sonoelastography in assessing solid renal masses.MethodsThis prospective research was steered on 26 individuals diagnosed with a solid renal mass, as endorsed by pathological analysis after surgical removal or biopsy. Elastography was performed on all patients. The measurement of strain index values for tissues was achieved by placing regions of interest of equal or near-equal size on both the tumor (A) and the adjacent normal renal cortex (B).ResultsStrain elastography showed no correlation with patient’s age, size of mass and probe to mass distance with p > 0.05 all. Sensitivity analysis showed that strain index can significantly predict malignant renal masses (P = 0.003) using a cut-off point 2, with 92.9% area under curve, 95.2% sensitivity, 80% specificity, 80% negative predictive value, 95.2% positive predictive value, and overall diagnostic accuracy 92.3%. Strain index > 2 was an independent predictor for malignant renal masses (P = 0.025), odds ratio 7.29 when adjusting for other risk factors. Malignant renal masses were significantly higher strain index compared to benign lesions with (P = 0.001).ConclusionsStrain elastography is a valuable technique for distinguishing between malignant and benign solid renal tumors. Benign lesions have lower strain index values compared to malignant ones, making the strain index a useful screening tool for distinguishing between benign and malignant renal masses using cut-off point 2.

Fractional viscoelastic constitutive modelling of real-time strain response for asphalt pavement composites subjected to simulating wheel loadings

ABSTRACT This paper proposed a replacement of the Kelvin–Voigt model with fractional basic elements, and the model constructed with this method was used to describe the viscoelastic strain response of asphalt pavement materials under the simulated wheel loading. Similarities between Abel element and Kelvin–Voigt model were explored, and strain response analytical solutions of Burgers and 1S1A1D models under intermittent have sine loading were derived. Then, different intermittent loading tests of asphalt binder and mixture were carried out. The results showed that both the Abel element and the Kelvin–Voigt model could represent the delayed viscoelastic behaviours accurately, and the strain response represented by them could be fully recovered during the unloading stage. Furthermore, parameters of the Abel element and Kelvin–Voigt model showed a high degree of correlation, and the 1S1A1D model exhibited the best effects for asphalt binders, particularly during the unloading period, with correlation coefficients mostly above 0.999. Moreover, when expressing the strain response of asphalt mixture under simulated wheel loading, the 1S1A1D model outperformed the Burgers model in terms of peak strain value, residual strain and changing trends. This study demonstrated that the fractional constitutive model was advantageous in representing the strain response of asphalt pavement composites.

Discontinuous Deformation Monitoring of Smart Aerospace Structures Based on Hybrid Reconstruction Strategy and Fiber Bragg Grating.

A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment.

On-line cure monitoring of phenol formaldehyde resin using embedded fiber Bragg grating sensor

On-line cure monitoring techniques have shown advantages for identifying the stages of curing and can lead to smart curing process of resin system and its composites. In this study, an on-line measuring technique with fiber Bragg grating (FBG) sensors was used to gain clear understanding of the curing stages of phenolic resin, as well as to determine its chemical shrinkage level. The FBG sensors were embedded in phenolic resin and worked under temperature raising and isothermal conditions. Through the interface between FBG sensor and resin, curing shrinkage strain was transferred to the grating, changing the central wavelength of FBG and producing strain. The feasibility of FBG sensors for monitoring chemical shrinkage during curing was verified by employing epoxy and benzoxazine resins that exhibits low chemical shrinkage. For cure monitoring of phenolic resin, the results show that the strain signal can be corresponded to various responses, such as solvent volatilization, hydroxylation and dehydration condensation reactions during temperature raising curing process. Through these strain characteristics, the curing state of phenolic resin can be obtained, which degree of cure ranges from 0.05 to 0.9. In isothermal curing process of phenolic resin, an exponential correlation between chemical shrinkage induced compressive strain and degree of cure of resin was identified, indicating that the real-time compressive strain is proportional to the degree of cure of phenolic resin. By evaluating real-time curing shrinkage levels, the estimation of curing degree of phenolic resin can be achieved utilizing exponential correlation of curing degree and curing shrinkage level.

Multifunctional asymmetric foam with Compression-Enhanced EMI shielding effectiveness exhibits Real-Time tunability of Reflection/Absorption ratio and pressure sensing

Traditional electromagnetic interference (EMI) shielding materials usually have constant shielding effectiveness and fixed absorption/reflection ratio, and cannot respond to the real-time changing shielding requirements of smart wearable electronic devices. Herein, smart asymmetric foams (AF) composed of chromium dioxide decorated graphene (CrO2@G)/thermoplastic polyurethane-poly vinyl alcohol (TP) foams and a eutectic gallium-indium liquid metal (LM)/TP layer were developed via an efficient ‘split conductive modular design/assembly’ strategy. The AF with compressive stress-controlled conductivity can capture and attenuate electromagnetic (EM) waves in the form of absorption-dominated or reflection-dominated by adjusting the compressive strain in real time. The optimized AF achieved a low average reflection coefficient (R) of 0.12 and a minimum value of 0.05, holding the lowest record for LM-based EMI shielding materials with comparable SEt. As the compressive strain increases from 0 to 90 %, the AF alter its SEt from 63.8 ± 3 dB to 92.7 ± 2 dB, while reducing the average EM wave absorption efficiency from 88 % to 39 %, allowing it to be adjusted in real time as needed to block EM waves. Furthermore, the AF demonstrated its application as a strain sensor to monitor human motions. This work provides an effective strategy for the design and preparation of multifunctional smart EMI shielding materials.

Virtual sensing for real-time strain field estimation and its verification on a laboratory-scale jacket structure under water waves

This study aims to achieve real-time estimation of the full-field strain distribution in a structure by signals measured from several strain gauges attached to the structure. Our virtual sensing procedure is developed based on finite element formulation and employs the mode superposition approach. To verify the feasibility of the proposed procedure, numerical and experimental tests are conducted on a laboratory-scale offshore jacket structure subjected to water waves. Key aspects addressed in this study include the selection of displacement modes and the division of strain signals. The experiments are performed in an ocean basin, and comprehensive explanations are provided for the jacket prototype design, implementations, experimental setup, and wave loading conditions. The performance of the proposed virtual sensing procedure is thoroughly assessed through various evaluation measures, enhancing the understanding of its capabilities and limitations in practical applications.

Experimental investigation on the load-bearing capacity and deformation behaviour of the composite lining with polyurethane compressible layer

Lining structures for tunnels in rheological rocks are susceptible to large deformations and damage. Installation of a polyurethane (PU) compressible layer between the primary and secondary lining provides a potential solution to mitigate large deformation hazards. Although the capability of PU-enhanced composite lining in adapting and absorbing large deformation has been found, its underlying load-bearing mechanism and deformation behaviour remain less understood. To fill the gap, large-scale model tests were conducted using a self-designed loading test setup, primarily focusing on the mechanical response of PU-enhanced composite lining. Ten lining specimens with varying thicknesses (25, 37.5, and 50 mm) and densities (50, 100, and 150 kg/m3) of the PU compressible layer were investigated. The digital image correlation (DIC) analysis technique was used to detect real-time deformation and strain fields under loading conditions. Results demonstrated that the PU compressible layer enhanced the ultimate load, toughness, and deformation capacity of the composite lining by an average of 17.6 %, 164.6 %, and 157.4 %, respectively. The load-displacement response of the specimens with PU compressible layer could be categorized into four stages: pre-cracking, post-cracking, strain-hardening, and failure. The primary lining experienced the largest vertical displacements among the specimens with the PU compressible layer, followed by the compressible layer and the secondary lining. Although the specimens exhibited similar progressive failure modes, there were variations in crack distribution and propagation. The analysis of rock-support interaction indicated that increasing the thickness of the PU compressible layer mainly affects the deformation capacity when the secondary lining reaches its elastic limit.

Evolution Characteristics of Void in the Caving Zone Using Fiber Optic Sensing.

Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It proposes a void zoning method based on fiber optic sensing for mining the overlying rock and, in combination with physical model experiments, systematically investigates the dimensions, distribution, and deformation characteristics of rock mass voids. By analyzing fiber optic sensing data, the correlation between the rate of void expansion and the stress state of the rock mass is revealed. The research results demonstrate that as mining progresses, the internal voids of the rock mass gradually expand, exhibiting complex spatial distribution patterns. During the mining process, the expansion of voids within the overlying collapsed rock mass is closely related to the stress state of the rock mass. The rate of void expansion is influenced by changes in stress, making stress regulation a key factor in preventing void expansion and rock mass instability. The application of fiber optic sensing technology allows for more accurate monitoring of changes in rock mass voids, enabling precise zoning of voids in the overlying collapsed rock mass during mining. This zoning method has been validated against traditional theoretical calculations and experimental results. This research expands our understanding of the evolution characteristics of voids in overlying collapsed rock mass and provides valuable reference for backfilling engineering practices and backfilling parameter optimization.

Multifunctional flexible graphene oxide/bacterial cellulose composite paper platforms for realtime monitoring sweat and strain in wearable devices

Paper-based wearable sensors have gained significant attention in sweat analysis due to their widespread availability, comfortable wearability, and hygroscopic behavior. However, integrating functional components such as the conducting polymers in one sensor to realize the simultaneous and on-demand detection for the physical and chemical indexes compromises their stable performance under operation due to mismatch among various functional parts. We report a stable, multifunctional, and scalable paper-based flexible sensing platform for improved performance for in-situ sweat stimulating and analyzing of sweat as well as the monitoring of body motion. The welded honeycomb structure of composite paper formed in direct laser writing can provide a reversible and precise response in resistance during bending. The internal confined space containing the bacterial cellulose can induce an electrostatic enrichment of hydrogen ions in sweat, thus enhancing the pH-sensing sensitivity (68 mV pH-1). This spatial separating design for the two detections can further isolate the interfering, thus enabling the simultaneous and highly sensitive analysis of pH and movement. In addition, the controlled joule heating effect and the nano-fluid channels of the papers can help generate and rapidly transfer the sweat, further facilitating an on-demand and rapid responsive detection.

Endoscopic ultrasonography in diagnosis of digestive diseases. Review of clinical cases

Endoscopic ultrasound (EUS) is a high-tech ultrasound examination that simultaneously combines the options of endoscopic and ultrasound diagnosis of diseases of the gastrointestinal tract, pancreas, bile ducts and liver. The advantages of endoscopic ultrasound over traditional transabdominal ultrasound are that the ultrasound transducer can be guided directly through the lumen of the gastrointestinal tract to the object under visual control. The EUS is the method of choice for the study of submucosal lesions in the upper gastrointestinal tract. It is the most accurate method for the detection and diagnosis of submucosal formations due to its high sensitivity, specificity, is used as the next research method after endoscopy and can provide information about the origin, size, borders, homogeneity, as well as the choice of treatment method: endoscopic or surgical one. A more important application of EUS is the determination of the stage of malignant neoplasms of the gastrointestinal tract, as it determines the treatment and predicts the disease. And the real-time strain elastography, which allows analyzing the stiffness of tissues, helps in this. The high accuracy of this study allows for the differential diagnosis of benign and malignant neoplasms. The article presents cases of examination of patients with submucosal neoplasms of the stomach who were treated in the SI “Institute of Gastroentero­logy of the National Academy of Medical Sciences of Ukraine”.

CFRP drive shaft damage identification and localization based on FBG sensing network and GWO-BP neural networks

Carbon Fiber Reinforced Plastic (CFRP) has the advantages of light weight, high strength, small coefficient of thermal expansion, good vibration damping performance, etc., the use of its production drive shaft can reduce weight, improve the intrinsic frequency, transmission efficiency and precision, and has been used in helicopters, heavy duty machine tools, wind turbines, ships and other high-end equipment. Once the damage occurs, it will cause adverse effects such as reduction of mechanical efficiency of the system, degradation of transmission system performance, and decrease of reliability and safety. The existing non-destructive testing technology for carbon fiber reinforced plastic has limitations such as inconvenient use, complex equipment, and no real-time online monitoring. To address this problem, this paper adopts Fiber Bragg Gratings (FBG) sensors to obtain real-time strain field data of CFRP drive shaft, and uses the linear and nonlinear mapping ability of Back Propagation Neural Network (BP neural network) to identify and locate the damage of CFRP drive shaft. At the same time, the GWO optimization algorithm is used to optimize the BP neural network to construct a CFRP drive shaft damage identification and localization system based on FBG sensing network and GWO-BP neural network. The experiments show that the CFRP drive shaft damage identification and localization system constructed in this paper can obtain the CFRP drive shaft strain field information in real time and accurately identify the damage and its location.

Strain/displacement field reconstruction and load identification of bracket arm structure of high-speed maglev train based on superposition conversion algorithm

The bracket arm structure, subjected to long-term vertical levitation and transversal guidance forces, is a key load-bearing component of high-speed maglev trains. It is crucial to monitor strain, displacement, and load in real time to evaluate structural health. However, it is almost impossible to measure directly at the service site of trains due to the intricacy of the bracket arm structure and the constrained installation area. Hence, it is an effective and feasible approach to reconstruct the strain/displacement field using a limited amount of strain information from FBG sensors. In order to reconstruct the strain/displacement field and identify the load on complicated structures, a load–strain/displacement superposition conversion algorithm is proposed in this paper. The modal coordinate and the mode-shape matrix are swapped out for the load weight and the load–strain/displacement matrix, respectively. Thus, it gets around the difficulty of choosing the mode order in traditional modal theory. In addition, the algorithm can achieve full-field reconstruction of both strain and displacement by measuring only part of the nodal strain response, reducing the redundancy of the traditional algorithm. Finally, numerical simulations and experimental tests were carried out to verify the feasibility of the proposed method for field reconstruction and load identification.

Study of elastoplastic deformation and crack evolution mechanism of single-crystal ice during uniaxial compression using 3D digital image correlation

Single-crystal freshwater ice was subjected to uniaxial compression tests with different strain rates and crystal orientations, and the real-time full-field deformation and strain were measured by the three-dimensional digital image correlation (3D DIC) method. The mechanism of elastoplastic deformation and crack evolution of single-crystal ice is discussed based on the basal slip. The maximum compressive strength appeared when the crystal orientation (angle between the C-axis and loading direction) θ=0°/90°. When 45°<θ<90°, an inclined bearing layer appeared and caused the specimen to rotate simultaneously. When 0°<θ⩽45°, the vertical compressive stress was sequentially transmitted through the basal plane, two diagonal expansions to the sidewall appeared. The failure mode and compressive strength of ice exhibited a ductile–brittle transition. The strength was positively correlated with the strain rate below the ductile–brittle transition range. The stress concentration at the crack tip was released through basal slip, inhibiting crack propagation, which showed multi-crack ductile failure. When in the ductile–brittle transition range, the cracks could grow stably, leading to axial splitting failure. When the strain rate was higher, the crack tip could not grow stably, producing inclined cracks and resulting in spalling or axial splitting failure, and the ice exhibited a brittle failure mode.

Associations of Real-Time Ultrasound and Strain and Shear Wave Elastography with Gastrointestinal Organs: A Systematic Review.

Ultrasound elastography is gaining attention for its diagnostic potential across various medical fields, and its physical properties make it valuable in modern clinical medicine. However, its specific attributes, especially in the context of recent medical advancements, remain relatively unexplored. This study aimed to identify instrument-specific characteristics and applications of real-time ultrasound elastography, shear wave elastography, and strain elastography, particularly within gastroenterology. Following PRISMA guidelines, the study examined elastography articles on databases like PubMed, resulting in 78 included articles. Data on patient demographics, organ involvement, specificity, sensitivity, accuracy, positive predictive value, and negative predictive value were extracted. Statistical analysis involved SPSS version 21, with significance set at p < 0.05. The majority of patients were male (50.50%), with a mean age of 42.73 ± 4.41 years. Shear wave elastography was the most prevalent technique (48.7%), and liver investigations were predominant in gastroenterology (34.6%). Gastrointestinal applications showed higher sensitivity, positive predictive value, and negative predictive values (p < 0.05) but lower specificity (p < 0.05). Real-time ultrasound elastography exhibited increased specificity, accuracy, and predictive values (p < 0.05). Ultrasound elastography appears more accurate and effective in gastroenterological settings. Nonetheless, its performance depends on instrument-specific and operator-dependent factors. While promising, further studies are necessary to ascertain optimal utilization in both gastrointestinal and non-gastrointestinal conditions.