Type Of Architecture Research Articles

First Detection of Podocytes in the Circulatory System of Enigmatic Echiurids (Annelida: Thalassematidae).

The fine structure of echiurid blood vessels in the proboscis is known in detail, but the circulatory system of the trunk is still understood mainly at the level of general anatomy. The trunk circulatory system was studied in Bonellia viridis females, and specialized podocytes were found to form the walls of the ring vessel and the anterior part of the ventral vessel. Podocytes were for the first time described in the echiurid circulatory system. Podocytes of B. viridis displayed a typical cell architecture, which is known for other bilaterians. A podocyte consists of a cell body; primary processes; and pedicels, which extend from the primary processes and are interconnected via specialized slit diaphragms. The presence of podocytes indicates that the ventral and ring vessels act as ultrafiltration sites, where the plasma is filtered through the basal lamina into the body cavity.

3D packaging and Moore’s Law

Abstract This paper describes how the semiconductor industry has seen steady improvements in key metrics such as performance, power consumption, and area, but as the semiconductor industry shrinks in device size and the technical challenges of ever-increasing performance demands, it is critical to improve chip performance for maximizing the operating frequency of individual ICs and reducing their power consumption. But when talking about improving chip performance, it is important not to focus solely on the individual integrated circuit (IC), but to evaluate it as part of the overall system. For example, as microprocessor frequency increases, so does the demand for main memory data. However, if the connections between the chips that transfer data to the processor do not keep pace with the increase in performance, these connections can become a bottleneck in the performance of the entire system. Instead, this paper describes a typical architecture for semiconductor 3D integration, highlights the technology choices for 3D integration implementation, and analyzes and explains the design challenges it presents. After outlining the above, this paper also analyzes the applications and achievements of 3D integration technology in recent years.

Correlation between the expression of the iNOS, caspase-3 and α-SMA genes in the parotid glands of albino rats following the administration of two antihistamines from two different generations.

Several medications, including antihistamines, can alter salivary gland function, causing dry mouth or xerostomia. Antihistamines are commonly used for treating allergic rhinitis. The aim of the present study was to compare and correlate the effects of first-generation vs. second-generation H1-antihistamines on the parotid glands of rats. Twelve adult male albino rats were used; 4 rats served as a control group (group I) and the remaining rats were divided into 2 groups: group II received promethazine hydrochloride; and group III received cetirizine dihydrochloride for 3 weeks. The parotid salivary glands were dissected, and examined histologically and analyzed histomorphometrically for the acinar area percentage. In addition, mRNA gene expression of iNOS, caspase-3 and α-SMA was assessed using quantitative realtime polymerase chain reaction (qRT-PCR). Finally, all the obtained data was statistically analyzed. Histologically, group I showed the typical architecture of the gland. In group II, degenerative changes were noticed, including acinar degeneration and shrinkage with widened connective tissue septa, intracellular vacuolization, and increased inflammatory cell infiltration. In group III, similar histological features were detected as in group II, but to a lesser extent. Histomorphometric results revealed significant differences in the acinar area percentage between various groups. In addition, qRT-PCR results showed a significant increase in iNOS expression in both groups II and III as compared to group I, caspase-3 gene expression was significantly increased in group II, while in group III, it increased non-significantly. Finally, α-SMA gene expression non-significantly decreased in both groups II and III. A significant positive correlation was observed between caspase-3 and iNOS gene expression, while an inverse correlation was noticed between caspase-3 and α-SMA gene expression. The administration of antihistamines resulted in changes in the rat salivary glands, which could be due to the induction of oxidative stress and the resultant apoptotic effect. These changes were suggested to occur mainly through action on muscarinic receptors; yet, action on histamine receptors could not be excluded. However; these effects were less marked with the second-generation antihistamine.

Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression

In recent years, model compression techniques have gained significant attention as a means to reduce the computational and memory requirements of deep neural networks. Knowledge distillation and pruning are two prominent approaches in this domain, each offering unique advantages in achieving model efficiency. This paper investigates the combined effects of knowledge distillation and two pruning strategies, weight pruning and channel pruning, on enhancing compression efficiency and model performance. The study introduces a metric called “Performance Efficiency” to evaluate the impact of these pruning strategies on model compression and performance. Our research is conducted on the popular datasets CIFAR-10 and CIFAR-100. We compared diverse model architectures, including ResNet, DenseNet, EfficientNet, and MobileNet. The results emphasize the efficacy of both weight and channel pruning in achieving model compression. However, a significant distinction emerges, with weight pruning showing superior performance across all four architecture types. We realized that the weight pruning method better adapts to knowledge distillation than channel pruning. Pruned models show a significant reduction in parameters without a significant reduction in accuracy.

Quantitative estimates of the metachromasia reaction of volutin granules of yeast using neural networks

The metachromatic coloration of volutinous granules of the yeast Saccharomyces cerevisiae is one of the indicators of the influence of sharp geomagnetic field (GMF) perturbations. The metachromasia reaction is based on the aggregation of dye molecules in interaction with inorganic polyphosphates, which are components of volutinous granules. To determine the characteristics of the geomagnetic field that cause the appearance of different colors of the metachromasia reaction, it is necessary to simultaneously monitor this reaction and changes in the GMF. High-quality monitoring is possible with rapid automated counting of cells with all possible color changes during the metachromasia reaction. The aim of the work was to develop a neural network architecture for recognizing and quantifying color changes and heterogeneity in real time during monitoring of the metachromasia reaction of volutinous granules of the yeast S. cerevisiae, which is necessary for further determining their correlations with changes in the geomagnetic field of different intensities. A program based on a nonrecursive labeling algorithm was created to count the number of cells in the study groups. In the course of the work, the software of two neural network architectures was compared to determine the best results in recognizing and quantifying yeast cells with different colors during the volutinous granule metachromasia reaction. It was determined that the Unet architecture type coped with the tasks of cell classification and segmentation much more efficiently than the Inception v3 architecture. The average relative error for automatic recognition of all cell groups was 3.85%, and the maximum relative error was 4.56%. The performance of the neural network was 89.9% when detecting cell segmentation and 86.4% when detecting color differences in the metachromasia reaction.

Expanding hardware accelerator system design space exploration with gem5-SALAMv2

With the prevalence of hardware accelerators as an integral part of the modern systems on chip (SoCs), the ability to model accelerators quickly and accurately within the system in which it operates is critical. This paper presents gem5-SALAMv2 as a novel system architecture for LLVM-based modeling and simulation of custom hardware accelerators integrated into the gem5 framework. It overcomes the inherent limitations of state-of-the-art trace-based pre-register-transfer level (RTL) simulators by offering a truly “execute-in-execute” LLVM-based model. It enables scalable modeling of multiple dynamically interacting accelerators with full-system simulation support. To create long-term sustainable expansion compatible with the gem5 system framework, gem5-SALAM offers a general-purpose and modular communication interface and memory hierarchy integrated into the gem5 ecosystem, streamlining designing and modeling accelerators for new and emerging applications. gem5-SALAMv2 expands upon the framework established in gem5-SALAMv1 with improved LLVM-based elaboration and simulation, improved and more extensible system integration, and new automations to simplify rapid prototyping and design space exploration. 11Conference Paper Extension: This work extends the work presented in gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling from MICRO 2020 (Rogers et al., 2020). This work expands on the aforementioned work by revamping the gem5-SALAM internals to provide more robust and extensible simulations, introducing new automation tools for expanding and simplifying design space exploration, and demonstrates the new capabilities of gem5-SALAMv2 by exploring multiple configurations of simple neural network architectures.Validation on the MachSuite (Reagen et al., 2014) benchmarks presents a timing estimation error of less than 1% against the Vivado High-Level Synthesis (HLS) tool. Results also show less than a 4% area and power estimation error against Synopsys Design Compiler. Additionally, system validation against implementations on an Ultrascale+ ZCU102 shows an average end-to-end timing error of less than 2%. Lastly, we demonstrate the upgraded capabilities of gem5-SALAMv2 by exploring accelerator platforms for two deep neural networks, LeNet5 and MobileNetv2. In these explorations, we demonstrate how gem5-SALAMv2 can simulate such systems and guide architectural optimizations for these types of accelerator-rich architectures. 22The most up-to-date version of gem5-SALAMv2 is available at https://github.com/TeCSAR-UNCC/gem5-SALAM..

MicroRNA-29b attenuates fibrosis in a rat model of Peyronie's disease.

Peyronie's disease is characterized by the formation of fibrotic plaques in the penile tunica albuginea. Effective treatments are limited, warranting the investigation of new promising therapies, such as the application of microRNAs that regulate fibrosis-related genes. We aimed to investigate the therapeutic potential of mimicking microRNA-29b in a fibrin-induced rat model of Peyronie's disease. The study was designed in two phases. To establish an optimal Peyronie's disease model, rats received either human fibrin and thrombin or saline solutions into the tunica albuginea on days 0 and 5. The animal model validation was done through expression and histopathological analyses, the latest by an experienced uropathologist. After validation, we performed microRNA-29b treatment on days 14, 21, and 28 of the study. This phase had control (normal saline) and scramble (microRNA scramble) groups. The mid-penile shaft was removed on day 30 for histological examination and molecular analyses in both study stages. The control group displayed typical tunica albuginea histologic architecture in the animal model validation. In Peyronie's disease group, the Hematoxylin and eosin and Masson Trichrome staining methods demonstrated an interstitial inflammatory process with concomitant dense fibrotic plaques as well as disarrangement of collagen fibers. Additionally, we found out that reduced microRNA-29b (p=0.05) was associated with significantly increased COL1A1 and transforming growth factor β1 genes and proteins (p>0.05) in the Peyronie's disease group. After treatment with mimic microRNA-29b stimulation, the Hematoxylin & eosin and Masson Trichrome staining revealed a discrete and less dense fibrotic plaque. This result was associated with significantly decreasing expression of COL1A1, COL3A1, and transforming growth factor β1 genes and proteins (p<0.05). The fibrin-induced animal model showed significant histopathological and molecular changes compared to the Control group, suggesting that our model was appropriate. Previous findings have shown that increased expression of microRNA-29b was associated with decreased pathological fibrosis. In the present study, treatment with microRNA-29b decreased the gene and protein expression of collagens and transforming growth factor β1. This study reveals the therapeutic potential for Peyronie's disease involving molecular targets. MicroRNA-29b application on the rat's tunica albuginea attenuated fibrosis, arising as a novel potential strategy for Peyronie's disease management.

Confrontation and cooperation between timber-imitating appearance and brick masonry in the Wuliang Hall: a case study of Manjusri Hall and Samantabhadra Hall of Longchang Temple

ABSTRACT The Manjusri Hall and Samantabhadra Hall of Longchang Temple in Baohua Mountain are representative of a series of Wuliang Halls, a distinctive architectural heritage type in China. To better understand the technical and artistic characteristics of the timber imitation by bricks of the Wuliang Hall, the spatial, formal, and structural features of the two halls are analyzed. Firstly, an accurate survey of the architectural heritage is conducted to obtain precise geometric dimensions, enabling analyses of spatial and formal aspects of the hall. Subsequently, the structural performance of the hall under static load is analyzed by finite element method. The results indicate that barrel vaults and arches of different sizes serve distinct spatial functions. The appearance form of the Wuliang Hall generally aligns with the style of the Ming and Qing official timber buildings, while the detailed construction methods may be adjusted due to material or structural considerations. Vault and arch structures are employed to adapt to brick masonry, but the sill walls, tops and imposts of arches and vaults are prone to tensile damage under vertical loads or seismic actions. These results could contribute to the interpretation of the technical motivations and human ideologies for Chinese masonry timber-imitating structures, and making conservation strategies for buildings of this type.

Prospects for the houseboats and floating houses operation in Primorsky region

The article presents the results of a study the possibilities and necessary conditions for the use of houseboats and floating houses in the Primorsky region. It is shown the dynamics and general tendencies of the tourist flow in the Primorsky region over the past ten years. A brief history of floating houses appearance and respective reasons are shown. The architectural and structural types, design features, life support systems, features of their internal saturation and layout are considered. A classification of floating houses according to various criteria is proposed. Modern systems and technologies that ensure autonomy and energy independence of these objects are considered. The operation features, maintenance and periodic repair specifics of floating houses of different types during different periods of their operation are noted. The regional, natural and hydrometeorological features of the Primorsky region facilitating and complicating the use of these objects for the development of sea and island tourism are analyzed. The results of the work are part of a study aimed to the development of maritime transport technology and marine tourism, accessibility of remote areas and increasing the efficiency of the transport and logistics system of the Primorsky region taken as a whole.

SLOW TOURISM AS A CONTEMPORARY TENDENCY IN THE TOURISM MARKET: IMAGE CONTENT AND GEOTAG ANALYSIS ON INSTAGRAM

Although it originated from the Slow Food and Cittaslow movements, slow tourism has spread to many spheres of tourism. The purpose of the paper is to investigate what people associate with slow tourism, and how and where they engage in slow tourism. Social media content analysis on Instagram, using hashtag #slowtourism, was performed in the research. The general conclusion is that people mostly associate slow tourism with different forms of architecture and landscape, and to lesser extent with transport and food. Also, there was significant content variation within the architecture and landscape categories, suggesting that people associate different types of architecture and tourism destinations with slow tourism. When it comes to location, the majority of photos were taken in Italy, which is not surprising, considering that Slow Food and Cittaslow movements originated in this country.

Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates

Abstract Structural health monitoring (SHM) data is the essential foundation for any SHM structural integrity assessment, including large civil infrastructure such as the miter gate application in this work. For some applications, the amount of monitoring data is limited due to various reasons such as a lack of sensor deployment investment, sensor reliability, inaccessibility of measurement locations, expensive duty cycles, etc. This limited data could result in uncertainty in structural health assessment. This paper addresses this challenging issue by proposing a data augmentation method based on image translation for Bayesian inference-based damage diagnostics. In particular, we translate the monitoring data of one miter gate to that of another, thereby increasing the volume of monitoring data available for assessing the structural health of a target miter gate. This translation starts with converting the monitoring data of different miter gates into images. After that, Cycle Generative Adversarial Networks (CycleGAN) are employed to accomplish the task of image translation among different miter gates. The translated images (i.e., synthetic monitoring data) are then used together with the true monitoring data for damage diagnostics. Two types of CycleGAN architectures are investigated and compared using a case study. Results of the case study show that the proposed data augmentation method can effectively improve the accuracy and confidence of damage diagnostics of miter gates. It demonstrates the potential of integrating synthetic data generation with probabilistic model updating in structural health monitoring.

Applicability of pre-trained CNNs in temperate deforestation detection

ABSTRACT Technological advancements have opened up new possibilities for accurately identifying deforested areas and developing effective data collection strategies. Our paper proposes a practical approach to improve deforestation monitoring using satellite images and Convolutional Neural Networks (CNNs) for image classification. To train the CNNs, we used labeled data primarily sourced from the Amazon basin, while images from the Romanian Carpathian mountains were reserved for testing. Building on this foundation, we evaluated various pre-trained CNN architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, GoogLeNet, and Swin Transformer, comparing their performance with a typical CNN architecture. In addition, we refined the performance by testing four ensemble learning methods. We found a decrease of only 10% in accuracy for using the models on data from the temperate area. Our findings contribute to this interdisciplinary field by providing insights into the effectiveness of pre-trained CNNs in classifying satellite images from temperate regions, trained with knowledge from tropical deforestation data. This contribution may open ways for the development of a quasi real-time deforestation monitoring system and serve as a reference for future research in temperate areas.

CFD stability improvement using dynamic mode decomposition of solution update vectors

We present a novel method for improving the numerical stability of finite volume simulations by optimizing the mesh through dynamic mode decomposition of solution update vectors. Our approach leverages dynamic mode decomposition to approximate the non-linear solution evolution as a linear mapping, enabling us to represent the dynamic system with fewer degrees of freedom. By conducting eigenanalysis on the reduced system, we gain insights into the growth rate and oscillation frequency of dominant solution modes. We then identify the control volumes and vertices that have a significant influence on each dynamic solution mode. We compute the gradients of the Jacobian diagonal elements with respect to the movement of the selected vertices. Based on these gradients, we adjust the positions of the vertices to improve the diagonal dominance of the corresponding Jacobian rows. We utilize this approach in conjunction with our in-house flow solver as well as with Ansys Fluent to test its effectiveness when applied to different types of CFD software architecture. We show that the presented methodology is fully non-invasive to the host flow solver and does not require any modifications or access to the source code. Our approach offers a substantial computational cost reduction compared to existing methods for numerical stability improvement. Various results demonstrate the strength and efficacy of this state-of-the-art approach for improving numerical stability through mesh optimization.

Tailoring a Two-Dimensional Halide Perovskite Composed of the Secondary Amine Cation for Anisotropic X-ray Responses.

Two-dimensional (2D) metal-halide perovskites have shown broad application prospects in the field of optoelectronic detection. The presence of the natural quantum-well structure results in strong anisotropy of physical properties, while studies on anisotropic X-ray responses remain insufficient. Here, we present an intriguing anisotropy of X-ray-responsive behaviors in a 2D halide perovskite, (t-ACH)2(DMA)Pb2Br7 (1, where t-ACH is trans-4-(aminomethyl)cyclohexanecarboxylate and DMA is dimethylamine), in which the secondary amine DMA+ cation with a large ionic radius locates inside the perovskite cage to form inorganic frameworks. The alternative alignment of inorganic slabs and organic bilayers creates a typical quantum-well architecture, which accounts for the generation of photoelectronic anisotropy. High-quality crystals of 1 exhibit notable semiconducting properties with a large μτ product (1.9 × 10-4 cm2 V-1). Intriguingly, 1 has better X-ray detection sensitivity (∼569.9 μC Gyair-1 cm-2) along the in-plane direction, which is attributed to its excellent charge carrier transport performance in this direction. Conversely, the higher resistance stemming from the organic barrier results in a lower detection limit along the out-of-plane direction (∼78.1 nGyair s-1), much lower than the medical diagnostic criteria (∼5.5 μGyair s-1). This work might open up new possibilities for the creative use of hybrid perovskites in direct X-ray detection.

BPP0974 is a Bordetella parapertussis adhesin expressed in the avirulent phase, implicated in biofilm formation and intracellular survival

B. parapertussis is a bacterium that causes whooping cough, a severe respiratory infection disease, that has shown an increased incidence in the population. Upon transmission through aerosol droplets, the initial steps of host colonization critically depend on the bacterial adhesins. We here described BPP0974, a B. parapertussis protein that exhibits the typical domain architecture of the large repetitive RTX adhesin family. BPP0974 was found to be retained in the bacterial membrane and secreted into the culture medium. This protein was found overexpressed in the avirulent phase of B. parapertussis, the phenotype proposed for initial host colonization. Interestingly, BPP0974 was found relevant for the biofilm formation as well as involved in the bacterial attachment to and survival within the respiratory epithelial cells. Taken together, our results suggest a role for BPP0974 in the early host colonization and pathogenesis of B. parapertussis.

ANN model of co-gasification process in bubbling fluidized bed: Contribution ratio analysis

Co-gasification of coal and biomass in a bubbling fluidized bed may offer an effective and economical method to partially replace coal to produce hydrogen, thereby addressing the objectives of achieving a carbon dioxide emissions peak and carbon neutrality. An Artificial neural network (ANN) model was utilized to simulate the hydrogen and other performance of co-gasification in the bubbling fluidized bed. Five types of ANN architectures were tested, including FFBP, CFBP, EFBP, LR, and NARX. Five optimization algorithms were also applied, including L-M, GA, GD, GDX, and PSO, to identify the optimal model to give a more accurate predicate the hydrogen production. The input data were derived from the ultimate analysis of the mixed coal and biomass, the proximate analyzes of mixed coal and biomass, and the operational conditions. The output data consisted of gas composition, carbon conversion, gasification efficiency, total gas yield, syngas yield, low and high heating values of the gas. The LR-PSO with 6 hidden neurons (R2 = 0.9995 and MSE = 0.1357) outperformed other ANN types and optimization algorithms. To further understand the performance of co-gasification, the influences of biomass to coal ratio, equivalence ratio, steam to carbon ratio and temperature on ratio of H2 to CO and syngas yield were predicted. The highest ratio of H2 to CO for the work of Li et al. (2010) [26], Song et al. (2013) [27] and Valdés et al. (2015) [28] were 0.7, 1.2 and 0.88, respectively. And those for syngas yield were 0.85, 1.09 and 1.55 m3/kg, respectively. To reveal the important factor on the ratio of H2 to CO, a detailed analyzed of the contribution ratio was conducted. The most critical factor affecting co-gasification performance in the work of Song et al. (2013) [27] were identified as SC ratio and temperature, which was higher than those of W and ER. The effect of SC exceeded that of T at SC ratio of 0.4, while it came to a reverse at temperature above 700 °C. These findings may serve as a valuable tool for adjusting the various factors to achieve the desired performance in the co-gasification of coal and biomass.

Machine learning-based anatomical segmentation: A systematic review of methodologies and applications

Image segmentation has established itself as a cornerstone of modern medical imaging practices, achieving state-of-the-art accuracy and playing a pivotal role in critical applications such as pathology diagnosis, treatment planning, tumour segmentation and organ delineation to name a few. The integration of deep learning and neural network techniques has sparked a dynamic and constantly evolving landscape, propelling rapid advancements in image segmentation. This work aims to provide an all encompassing review of the general state-of-the-art, with a focus on developments made within the last few years. Over 100 papers presenting development in the field have been considered, primarily published between 2020-2023, separated into their architectural type. Within each type, significant works and conclusions have been highlighted. We conclude the paper with a discussion of the challenges facing the segmentation landscape as a whole, and how these challenges have been best circumnavigated to date.

ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ РОЗПІЗНАВАННЯ ТЕРОРИСТИЧНИХ ТА ВОРОЖИХ ВІЙСЬКОВИХ ОБ’ЄКТІВ

The development of artificial intelligence technologies and their application in solving military tasks is the main concept of scientific advancement in Ukraine. Despite the availability of existing solutions for classifying limited types of civilian objects, there is a need to develop new technologies to gain an advantage over the enemy. Existing artificial intelligence models available in the public domain are oriented towards diagnosing restricted types of civilian objects. This limitation prevents the use of these models for analyzing military enemy equipment. Automating the process of recognizing enemy military objects by constructing a deep learning classification model. The process of building a classification model for enemy military equipment using deep neural networks is considered. The Sequential model type is chosen as the basis, which includes batch normalization layers added after each convolutional layer before further processing. Accelerated hyperparameter search is provided by the Random Search tool. The quality of classification models was measured using classical accuracy metrics. The implementation of the proposed solutions is carried out using the Python programming language and the Python Imaging Library, TensorFlow, Keras Tuner, and Matplotlib libraries. A convolutional neural network classification model has been constructed capable of classifying enemy military tanks and vehicles. The model demonstrates the best accuracy metrics on training/testing datasets of 0.6/0.55 with errors of 0.658/0.705, respectively. A comparative analysis of classification models with different types of architectures showed the advantage of the model in terms of accuracy and error rate when using an architecture that includes batch normalization layers. The created classification model is recommended for further training on new data or for solving computer vision tasks.

Trends in Hybrid Renewable Energy System (HRES) Applications: A Review

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.

Recognition of Brugada type 1 pattern on ECG by Echo State Networks

Abstract Background/Introduction The development of artificial intelligence (AI) and machine learning (ML) techniques, which can identify data patterns in electrocardiography, has led to the development of automated analysis tools for standard 12-lead ECGs. These tools are able to identify specific features associated with different cardiac diseases, which may be helpful in clinical practice [1]. One field of application is represented by Brugada Syndrome (BrS) diagnosis, which, despite characteristic ECG features, can be challenging to diagnose and correctly distinguish from other diseases. Purpose Within the Brugada Syndrome and Artificial Intelligence Applications to Diagnosis system study, we are developing an innovative model for diagnosing Type 1 BrS based on electrocardiographic pattern recognition through the Echo State Networks (ESN), a Recurrent Neural Network (RNN) type of architecture. Methods From patients enrolled in 5 Centers, 306 12-lead ECGs were obtained. The datasets were composed of spontaneous Type 1 pattern (coved) (group A, 98 patients); patients undergoing ajmaline test, classified as positive (group B, 47 patients) or negative (group C, 35 patients) according to test results; 220 controls with no clinical and familial history of arrhythmias (group D). ESN analyzed, as input, data collected from 1 beat of the ECG V2 lead. The datasets were used for the ESN model training and were assessed through a double cross-validation approach. The ESN performance was compared with the evaluation of 4 independent cardiologists, and sensitivity, specificity and accuracy were determined. Results The ESN model correctly classified 280/306 ECGs (91.5%), with a performance of 3.6 points superior to clinicians (269/306, 88.0%). ESN sensitivity (87.0% vs. 86.9%) and specificity (94.5% vs. 89.1%) are described in Table 1; accuracy in each group of the dataset (groups A, B, C, and D), represented in Table 2, were comparable to or greater than those of clinicians. Conclusion(s) In this work, an ESN-based system for diagnosing Type 1 BrS pattern on ECG was developed. Our results show that this ML model can detect ECG patterns associated with Type 1 BrS with accuracy, sensitivity and specificity comparable or superior to clinicians. In the future, we will use a larger dataset to increase the model performance further, with the real possibility of becoming a tool for everyday clinical practice.ESN and clinicians' diagnostic accuracyECG accuracy in each dataset group