Deep Structure Research Articles

Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.

Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual-model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively. The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall and F1 score. The consistency was assessed using the Dice similarity coefficient (DSC) and Cohen's Kappa. The mean absolute percentage error (MAPE) was calculated to assess the error of the model measurements of relative cross-sectional area (rCSA) and FI. Calculate the MAPE of FI measured by threshold algorithms to compare with the model. A total of 417 patients being evaluated, comprising 216 males and 201 females, with a mean age of 49 ± 15 years. In the internal test set, the muscle segmentation model achieved an overall DSC of 0.92 ± 0.10, recall of 92.60%, and AP of 0.98. The fat annotation model attained a recall of 91.30%, F1 Score of 0.82, and Cohen's Kappa of 0.76. However, there was a decrease on the external test set. For rCSA measurements, except for longissimus (10.89%), the MAPE of other muscles was less than 10%. When comparing the errors of FI for each paraspinal muscle, the MAPE of the model was lower than that of the threshold algorithm. The models demonstrate outstanding performance, with lower error in FI measurement compared to thresholding algorithms.

Generating in-store customer journeys from scratch with GPT architectures

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.Graphic abstract

A Miniature PMUT Array for Medical Applications

Abstract Wearable continuous monitoring technology and internal and cerebrovascular imaging are the frontier hot area of current scientific research, which are of great significance to improving human life and health. Ultrasonic imaging can obtain information such as deep tissue structure by transmitting ultrasonic waves into the tissue and detecting echo signals, and ultrasonic imaging technology has the advantages of high imaging depth, high resolution, high safety, fast and convenient, etc. It is one of the important technologies in the medical field such as wearable continuous monitoring technology and internal cardiovascular and cerebrovascular imaging. But miniaturization and low power consumption are necessary conditions for the success of such medical applications. Therefore, the ultrasonic imaging technology using a miniature ultrasonic probe is one of the most promising imaging technologies for this type of medical application. Aiming at the common technical problems of traditional ultrasonic transducers such as large volume, narrow bandwidth, high power consumption, difficulty in preparing two-dimensional arrays, and difficulty in integrating with ICs. In this paper, the electromechanical-acoustic multi-field coupling mechanism and array structure design technology of MEMS high-performance PMUT ultrasonic transducer elements are first studied, and a CMOS-compatible miniature PMUT high-performance array ultrasonic device suitable for this type of medical application is developed. The simulation results of the transducer are given. The measurement of various physical parameters of the transducer sample and the results of its application in the ultrasonic imaging system show that the miniature PMUT linear array ultrasonic sensor we designed is a kind of high performance ultrasonic array sensors that can be widely used in wearable continuous monitoring technology and internal cardiovascular and cerebrovascular imaging.

Composite fault diagnosis of gearbox based on deep graph residual convolutional network

In gearbox systems, a composite fault diagnosis resulting from mutual interference among different components poses a significant challenge. The traditional composite fault diagnosis methods based on conventional signal analyses and feature extractions often suffer from low sensitivity to fault characteristics and difficulty in effectively identifying composite faults. On the other hand, composite fault diagnosis research via deep learning and data-driven approaches typically faces issues such as incomplete training datasets and insufficient exploration of feature correlation information, leading to an underutilization of the fault information. Therefore, this paper proposes a deep graph residual convolutional neural network (DGRCN) based on feature correlation mining for composite fault diagnosis in gearboxes. First, Pearson correlation coefficients are utilized to explore the relationships among features in the traditional feature set, transforming these relationships into a graph-structured feature set. Next, a deep graph residual convolutional network is constructed by integrating deep graph structures into a residual framework. This network globally extracts composite fault subgraph features and explores local feature correlations. Finally, the model is trained via various composite fault datasets under complex working conditions, achieving the diagnosis and identification of composite faults under the constraint of limited samples. The experimental results demonstrate that the proposed method significantly improves composite fault diagnosis accuracy, outperforming commonly used methods in this field.

Review on Welding Process Monitoring Based on Deep Learning using Time-Series Data

The quality of welds during welding processes significantly affects the performance and the reliability of the final products. Therefore, to guarantee a high quality of the products, technologies that utilize time-series data measured by various sensors for monitoring the welding processes are required. Because the time-series data measured during the welding processes exhibit nonlinear and nonstationary characteristics, deep learning techniques, which can automatically learn the features of nonlinear and nonstationary signals through deep network structures, have recently gained recognition as a new monitoring method. Therefore, in this review, recent research that applied deep learning models based on time-series data measured during welding processes to monitor welding processes are introduced. In addition, the types of time-series data and deep learning model structures that are predominantly used to monitor the welding processes, such as predicting the penetration states and identifying the welding defects are discussed. Lastly, based on the research cases discussed herein, future research directions and the prospects of deep learning-based welding process monitoring technology that uses time-series data are discussed.

LDMNet: Enhancing the Segmentation Capabilities of Unmanned Surface Vehicles in Complex Waterway Scenarios

Semantic segmentation-based Complex Waterway Scene Understanding has shown great promise in the environmental perception of Unmanned Surface Vehicles. Existing methods struggle with estimating the edges of obstacles under conditions of blurred water surfaces. To address this, we propose the Lightweight Dual-branch Mamba Network (LDMNet), which includes a CNN-based Deep Dual-branch Network for extracting image features and a Mamba-based fusion module for aggregating and integrating global information. Specifically, we improve the Deep Dual-branch Network structure by incorporating multiple Atrous branches for local fusion; we design a Convolution-based Recombine Attention Module, which serves as the gate activation condition for Mamba-2 to enhance feature interaction and global information fusion from both spatial and channel dimensions. Moreover, to tackle the directional sensitivity of image serialization and the impact of the State Space Model’s forgetting strategy on non-causal data modeling, we introduce a Hilbert curve scanning mechanism to achieve multi-scale feature serialization. By stacking feature sequences, we alleviate the local bias of Mamba-2 towards image sequence data. LDMNet integrates the Deep Dual-branch Network, Recombine Attention, and Mamba-2 blocks, effectively capturing the long-range dependencies and multi-scale global context information of Complex Waterway Scene images. The experimental results on four benchmarks show that the proposed LDMNet significantly improves obstacle edge segmentation performance and outperforms existing methods across various performance metrics.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Identifying anticipated challenges when implementing group care: Context-analyses across seven countries to develop an anticipated challenges framework

ProblemDespite increasing interest in Group Care worldwide, implementation is challenging. BackgroundGroup Care is an evidence-based perinatal care model including three core components: health assessment, interactive learning, and community building. It has several advantages for service users and providers compared to individual perinatal care. AimWe aimed to identify anticipated challenges when implementing Group Care, and to develop a supporting tool based on these challenges. MethodsContext analyses through Rapid Qualitative Inquiries were conducted in 26 sites in seven countries to gain insight into the anticipated challenges when implementing Group Care. Data triangulation and investigator triangulation were applied. The context analyses generated 330 semi-structured interviews with service users and other stakeholders, 10 focus group discussions, and 56 review meetings with the research teams. FindingsWe identified six surface structure anticipated challenges categories (content, materials, facilitators, timing, location, group composition), and five deep structure anticipated challenges categories (health assessment, scheduling Group Care into regular care, enrolment, (possible) partner organisations, financials) occurring in all participating sites, leading to the development of the Anticipated Challenges Framework. ConclusionCompleting the Anticipated Challenges Framework raises awareness of anticipated challenges if sustainable Group Care implementation is to succeed and encourages the initiation of a concrete action plan to tackle these challenges. Application of the framework may offer important insights to health systems administrators and other key stakeholders before implementing Group Care. In the medium- and long-term, insights gained may lead to greater possibilities for sustainability and to the most cost-effective approaches for implementing Group Care.

Tunable Nanoislands Decorated Tapered Optical Fibers Reveal Concurrent Contributions in Through-Fiber SERS Detection.

Creating plasmonic nanoparticles on a tapered optical fiber (TF) tip enables a remote surface-enhanced Raman scattering (SERS) sensing probe, ideal for challenging sampling scenarios like biological tissues, site-specific cells, on-site environmental monitoring, and deep brain structures. However, nanoparticle patterns fabricated from current bottom-up methods are mostly random, making geometry control difficult. Uneven statistical distribution, clustering, and multilayer deposition introduce uncertainty in correlating device performance with morphology. Ultimately, this limits the design of the best-performance remote SERS sensing probe. Here we employ a tunable solid-state dewetting method to create densely packed monolayer Au nanoislands with varied geometric parameters in direct contact with the silica TF surface. These patterns exhibit analyzable nanoparticle sizes, densities, and uniform distribution across the entire taper surface, enabling a systematic investigation of particle size, density, and analyte effects on the SERS performance of the through-fiber detection system. The study is focused on the SERS response of a widely employed benchmark molecule, rhodamine 6G (R6G), and serotonin, a highly relevant neurotransmitter for the neuroscience field. The numerical simulations and limit of detection (LOD) experiments on R6G show that the increase of the total near-field enhancement volume promotes the SERS sensitivity of the probe. However, we observed a different behavior for serotonin linked to its interaction with the nanoparticle's surface. The obtained LOD is as low as 10-7 M, a value not achieved so far in a through-fiber detection scheme. Therefore, our work offers a strategy to design nanoparticle-based remote SERS sensing probes and provides new clues to discover and understand intricate plasmonic-driven chemical reactions.

Shallow water source depth discrimination based on a vertical linear array using deep learning

This study aims to explore source depth discrimination in shallow water waveguides using a vertical line array. Due to the similarity in physical characteristics of features and environmental parameters, the feature distributions of shallow and deep sources overlap in feature space. This overlap is further exacerbated by strong background noise, reducing detection reliability. Therefore, a deep learning-based source depth discrimination (DL-SDD) scheme is proposed. Specifically, this scheme is based on a residual structure, embedding channel attention mechanisms into the deep structure to eliminate noise-related information gradually. Furthermore, a specially designed loss function considers inter-class and intra-class distances to achieve compact and mutually distant distributions of source features. When this loss function is applied, the overlap of source feature distributions is suppressed in end-to-end feature learning, leading to a high detection probability. The numerical simulations demonstrate that the proposed DL-SDD outperforms traditional method, achieving a 7 % increase in detection probability and a 15 % decrease in false alarm rate, even near the discrimination depth. Furthermore, the discrimination depth is reduced by nearly half. Experimental results from the South China Sea validate the effectiveness of the proposed DL-SDD.

Oppositions, joints, and targets: the attractors that are the glue of social interactions.

Social interactions are often analyzed by scoring segments of predefined behavior and then statistically assessing numerical and sequential patterns to identify the structure of the encounters. However, this approach can miss the dynamics of the animals' relationship over the course of the encounter, one that often involves invariant bonds, say a nose-to-nose orientation, with many different movements performed by both partners acting to counteract each other's attempts to break or maintain the relationship. Moreover, these invariant bonds can switch from one configuration to another during an interaction, leading from one stable configuration to another. It is this stepwise sequence of configurational stabilities that lead to functional outcomes, such as mating, aggression, or predation. By focusing on the sequence of invariant relational configurations, the deep structure of interactions can be discerned. This deep structure can then be used to differentiate between compensatory movements, no matter how seemingly stereotyped they may appear, from movement patterns which are restricted to a particular form when more than one option is available. A dynamic perspective requires suitable tools for analysis, and such tools are highlighted as needed in describing particular interactions.

Effect of acid-alcohol deep eutectic solvents-regulated lignin structure on subsequent pyrolysis products distribution

In lignin extraction and depolymerization, alcohol reagents are often used to stabilize carbon positive ions or to reduce lignin condensation. In this paper, the effects of different deep eutectic solvent (DES) on the distribution and pyrolysis properties of lignin subsequent pyrolysis products were investigated by treating softwood kraft lignin (SKL) with different acidic DES (acid-alcohol system). It was shown that the elemental composition of lignin changed considerably after acidic DES treatment, and the theoretical calorific value of DES-modified lignin increased. FTIR and XPS analysis showed a slight increase in the content of alcohol hydroxyl groups in DES-treated lignin, which also indicated that ethylene glycol might have accessed to the macromolecular structure of lignin. Pyrolysis experiments indicated that the yield of the optimal pyrolysis oil was increased by DES treatment up to 40.3 % (7.0 % increase compared to SKL). Both H-type phenols and C-type phenols increased in pyrolysis oil (of which, the C-type phenols produced by DES-treated lignin pyrolysis ranged from 15.1 % to 18.9 %). In-situ infrared analysis of noncondensable gases produced by lignin pyrolysis also provided an indirect insight into the pattern of change in the active functional groups of lignin. CO2 was the main gas in the pyrolysis gas, and CH4 yield increased with the temperature increasing from 300 °C to 500 °C and then decreased with the increase of temperature from to 500 °C to 600 °C.

Deep Quasi-Recurrent Self-Attention with Dual Encoder-Decoder in Biomedical CT Image Segmentation.

Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in the literature, but the researchers are yet to be satisfied with their performance in analyzing biomedical Computed Tomography (CT) images. In this article, we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder-decoder. The proposed novel deep quasi-recurrent self-attention architecture evokes parameter reuse capability that offers consistency in learning and quick convergence of the model. Furthermore, the quasi-recurrent structure leverages the features acquired from the previous time points and elevates the segmentation quality. The model also efficiently addresses long-range dependencies through a selective focus on contextual information and hierarchical representation. Moreover, the dynamic and adaptive operation, incremental and efficient information processing of the deep quasi-recurrent self-attention structure leads to improved generalization across different scales and levels of abstraction. Along with the model, we innovate a new training strategy that fits with the proposed deep quasi-recurrent self-attention architecture. The model performance is evaluated on various publicly available CT scan datasets and compared with state-of-the-art models. The result shows that the proposed model outperforms them in segmentation quality and training speed. The model can assist physicians in improving the accuracy of medical diagnoses.

A model of contact deep groove seals based on partition model and JFO boundary condition

Unlike nuclear main pump seals, deep groove seals in aero-engines operate on a contact basis. However, the precise working mechanism and accurate quantitative methods for assessing their sealing performance remain elusive. This paper introduces a fluid-solid-thermal coupling model for contact deep groove seals, utilizing a partition model and considering JFO boundary condition, mixed lubrication, and detailed heat calculations. The model significantly enhances accuracy compared to original approaches. It comprehensively accounts for contact effects, thermal deformations, convergence wedges, radial waviness, hydrodynamic pressures, groove drainage, and convection, revealing characteristics of contact deep groove seals such as high stiffness, effective heat dissipation, wear resistance, and prolonged service life. The proposed model and working mechanism provide theoretical guidance for designing diverse deep groove seal structures.

Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging.

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

Lithospheric Folding Controls the Intracontinental Orogen of South China: Insight from Numerical Simulation

Abstract Orogenic processes worldwide have been attributed to various deformation mechanisms. However, the significance of lithospheric folding in these processes has often been overlooked and underestimated. Within the South China Block (SCB), a region marked by notable temporal and spatial variability in intracontinental deformation, the emergence of fold-and-thrust belts during the Paleozoic and Mesozoic periods has captured a scientific interest. The mechanisms governing the genesis of these belts remain a subject of debate, with no discernible subduction interface accounting for the extensive-scale fold-thrust deformation. Moreover, the SCB presents a substantial variation in lithospheric thickness, exceeding 100 km, offering a plausible mechanism for lithospheric folding. To interrogate this mechanism, we conducted lithospheric compression simulations via two-dimensional finite element methods, incorporating variable viscosity both laterally and vertically within the SCB. Our models elucidate that disparities in lithospheric strength beget distinctive deformational manifestation within the SCB. We observe that a weaker lithosphere tends to uplift, whereas a stronger lithosphere tends to subside during compression. Lithospheric strength also influences the Xuefengshan uplift and the spatial distribution of deformational features. In addition, lithospheric folding can account for crustal shortening and the presence of deep anomaly structures. A compelling correlation emerges between lithospheric folding and fluctuations in Moho depth and lithospheric thickness, suggesting its potential influence over the prolonged topographical evolution and shifts in depositional environments within the SCB. This study sheds new light on the role of lithospheric folding in the complex geodynamic history of the SCB and highlights its importance in understanding the broader context of orogenic processes worldwide.

Deep Geological Structure Analysis of the Dongyang Area, Fujian, China: Insights from Integrated Gravity and Magnetic Data

To explore the deep geological structure of the Dongyang area in Fujian, China, gravity data from the area and its surroundings were collected and processed. Additionally, a high-precision magnetic survey was conducted in the Zhongxian region of this area, with subsequent analysis of the magnetic anomalies. Through the integration of regional geological data, a comprehensive analysis was carried out on the characteristics of gravity–magnetic anomalies and deep geological structures in the Dongyang area. The study indicates that the primary portion of the Dongyang area lies southwest of the expansive circular volcanic structure spanning Dehua to Yongtai. Two significant residual gravity anomalies were identified within the region, interpreted as the Xiaoban-Shuangqishan and Dongyang-Lingtouping residual gravity-positive anomalies. In the Zhongxian region, the magnetic field exhibits complexity with notable amplitude variations. Positive anomalies predominate in the western and northern sectors, while localized positive anomalies are prominent in the eastern region. The central area portrays a circular and disordered mix of positive and negative anomalies. Particularly distinctive are the band-shaped and fan-shaped negative anomalies curving from northeast to southeast through the central region. Various positive and negative anomalies of varying strengths, gradients, and orientations overlay both positive and negative magnetic backgrounds in specific locales. Moreover, the Dongyang area showcases well-developed fault structures, primarily oriented in northeast and northwest directions. Leveraging the regional magnetic attributes in conjunction with regional geological data, 39 faults were deduced in the Zhongxian region of the Dongyang area, delineating three promising mineralization zones.

Deep structure of the Wulong goldfield, Liaodong Peninsula, China, revealed by receiver functions: implications for the tectonic and mineralization dynamics

During the Mesozoic, the North China Craton experienced intense tectonic movements that resulted in the formation of numerous gold deposits on the Liaodong and Jiaodong Peninsulas in northeastern China. To investigate the relationship between deep crustal structure and gold mineralization in the Liaodong Peninsula, we deployed 334 dense seismic stations in the Wulong goldfield (WLGF) with the idea of analysing numerous receiver functions at different array stations. The purpose focused on knowing the potential for gold mineralization in the area. The study revealed the following: (1) The WLGF is characterized by a crustal thickness of approximately 32 km and an average Vp/Vs ratio of 1.76. The high value of the Vp/Vs ratio near the Wulong gold deposit suggests that mantle materials have penetrated into the crust and contributed to the mineralization process. (2) A low-velocity layer located at a depth of 10–18 km below the WLGF seems to support the existence of a potentially brittle-ductile transition zone. Also, hydrothermal magma upwelling channels are observed in the upper crust beneath the Wulong gold deposit. (3) The presence of a discontinuous low-velocity layer in the middle crust beneath the Liaodong Peninsula suggests promising prospects for gold ore exploration. The receiver functions method based on a dense seismic array employed in this study can offer valuable references and guidance for the fine exploration and research of ore deposits in other regions globally.

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets.

Ultrasound Guided Internal Jugular Vein Acesss: Comparison Between Short Axis And Long Axis Technique

Abstract Background: Percutaneous techniques have transformed vascular cannulation. It has reduced the need for open procedures and the associatedwoundcomplications, but percutaneous techniques have made physicians dependent on relationships between surface anatomic landmarks and deep anatomic structures. Now, percutaneous central venous catheter insertion by physicians has a lesser complication rate. During central venous catheterization, the complications include infection, occlusion, thrombosis and mechanical complications. Aims and Objective: To relate the inner course of cannulating needle, familiarity of surface anatomic landmarks excludes the complication. Deep anatomic relationships visualization does not only improves the efficacy and safety of the procedure but also supplements the operator’s ability to identify difficult cannulations. The safety of internal jugular cannulations increases with ultrasound visualization. Central venous cathetrization (CVC) is done for hemodynamic monitoring (such as central venous pressure) long-term administration of fluids, antibiotics, total parental nutrition, and hemodialysis. Material and Methods: Most commonly, the internal jugular vein (IJV) is cannulated because of its relatively less complications and easy accessibility. Seldinger technique is used for IJV cannulation. Landmark-guided IJV cannulation is associated with a more number of complications compared to ultrasound-guided cannulation. Ultrasound-guided cannulation improves success rate, reduces the number of needle passes, and decreases complications. Two different real-time ultrasound-guided cannulations of IJV using a short axis/out-of-plane (SA) approach and a long axis/in plane (LA) approach are compared in the study. Hence, the present study was done at our tertiary care center to compare ultrasound-guided IJV access between SA and LA technique method with the number of attempts and complications. Results: It was observed in the present study that 4 (10%) patients in Long axis group had posterior wall puncture while 9 (22.5%) patients in short axis group had posterior wall puncture. Conclusion: Posterior wall puncture incidence is lesser in long axis group than short axis group.