Xiaoxue Zhou¹, Tingyang Yang², Ye Zhang¹, Yanping Ruan¹, Jiancheng Han¹, Xiaowei Liu¹, Ying Zhao¹, Xiaoyan Gu¹, Tingting Liu¹, Hairui Wang¹, Yihua He^1,*

Congenital Heart Disease, Vol.18, No.3, pp. 387-397, 2023, DOI:10.32604/chd.2023.029060

Abstract Background: Atrioventricular septal defects (AVSDs) are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography (FE). However, diagnosis on images is heavily depended on sonographers’ experience and the quantitative data are rarely studied. Objective: This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on FE. Methods: One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses retrospectively. The top six with the highest diagnostic accuracy rate were acquired according to the area under the curve (AUC), and the diagnostic value of six variables was analyzed.… More >

Ivana B. Cerović¹, Vladislav A. Vukomanović^1,2, Jovan Lj. Košutić^1,2, Mila S. Stajević^1,2, Sanja S. Ninić¹, Saša S. Popović¹, Ivan D. Dizdarević¹, Staša D. Krasić¹, Sergej M. Prijić^1,2,*

Congenital Heart Disease, Vol.18, No.3, pp. 361-371, 2023, DOI:10.32604/chd.2023.026533

Abstract Introduction: Transcatheter closure is an alternative to ventricular septal defect (VSD) occlusion surgery. Nit-Occlud Lê VSD coil is a new device yet to be evaluated. The study aimed to evaluate immediate and midterm results after transcatheter closure with the Nit-Occlud Lê VSD device. Methods: The retrospective analysis included 30 patients with VSD referred for closure during the period from October 2015 to December 2020. Results: At the time of intervention, the patients’ mean age and body weights were 7.5 ± 5.6 years and 29.3 ± 19.1 kg. The majority of the defects had perimembranous location (24/30), four defects had muscular and two outlet subaortic position.… More >

Nadeem Jabbar Chaudhry^1,*, M. Bilal Khan², M. Javaid Iqbal¹, Siddiqui Muhammad Yasir³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 245-259, 2022, DOI:10.32604/jai.2022.038875

Abstract Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product… More >

Hyunkyu Shin¹, Yonghan Ahn², Mihwa Song³, Heungbae Gil³, Jungsik Choi^4,*, Sanghyo Lee^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4753-4766, 2023, DOI:10.32604/cmc.2023.038362

Abstract Recently, convolutional neural network (CNN)-based visual inspection has been developed to detect defects on building surfaces automatically. The CNN model demonstrates remarkable accuracy in image data analysis; however, the predicted results have uncertainty in providing accurate information to users because of the “black box” problem in the deep learning model. Therefore, this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification. The visual representative gradient-weights class activation mapping (Grad-CAM) method is adopted to provide visually explainable information. A visualizing evaluation index is proposed to quantitatively analyze visual representations; this index reflects a rough estimate… More >

Sagheer Abbas¹, Shabib Aftab^1,2, Muhammad Adnan Khan^3,4, Taher M. Ghazal^5,6, Hussam Al Hamadi⁷, Chan Yeob Yeun^8,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6083-6100, 2023, DOI:10.32604/cmc.2023.037933

Abstract The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product quality while reducing the cost… More >

Ki-Yeol Eom¹, Byungseok Min^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336

Abstract Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors, autonomous vehicles, and artificial intelligence devices. However, there are few solutions to segment occluded objects in the X-ray inspection efficiently. In particular, in the Ball Grid Array inspection of X-ray images, it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls. In this paper, we present a novel automatic inspection algorithm that segments solder balls, and detects defects fast and efficiently when solder balls are occluded. The proposed algorithm consists of two stages. In the first stage, the… More >

Yu Chen¹, Sagar A. S. M. Sharifuzzaman², Hangxiang Wang¹, Yanfen Li¹, L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5455-5473, 2023, DOI:10.32604/cmc.2023.033787

Abstract The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which… More >

Min Zhang^*, Haixu Xi

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3911-3923, 2023, DOI:10.32604/csse.2023.036709

Abstract For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is… More >

Kiyotaka Go^1,2, Taichi Kato^2,*, Machiko Kito¹, Yoshihito Morimoto^1,2, Satoru Kawai¹, Hidenori Yamamoto², Yoshie Fukasawa², Kazushi Yasuda¹

Congenital Heart Disease, Vol.18, No.2, pp. 235-243, 2023, DOI:10.32604/chd.2023.027590

Abstract Background: There is no gold standard sedation method for pediatric cardiac catheterization. In congenital heart diseases with intracardiac shunts, hemodynamic parameters are prone to change depending on the ventilation conditions and anesthetics, although few studies have examined these effects. The purpose of this study was to investigate the effects of two different sedation methods on the hemodynamic parameters. Methods: This study retrospectively evaluated consecutive patients with ventricular septal defect (VSD) below 1 year of age who underwent cardiac catheterization at Aichi Children’s Health and Medical Center, who were divided into age- and VSD diameter-matched general anesthesia (GA) and monitored anesthesia… More >

Cherith Somerville^1,2, Kelsey Kalbfleisch^1,2, Roozbeh Manshaei^1,2, Qiliang Ding^1,2, John B.A. Okello^1,2,3, Rachel Silver⁴, David Chitayat^2,4, Varsha Thakur⁵, Olivier Villemain^5,6,7, Rebekah Jobling^1,2,8,*

Congenital Heart Disease, Vol.18, No.2, pp. 213-218, 2023, DOI:10.32604/chd.2023.023042

Abstract We report a three-year-old male child who presented with congenital valvular defects, right ventricular malformation, and initial developmental delay. Genome sequencing showed rare deleterious biallelic missense variants in PLD1. In his parents’ second pregnancy, echocardiogram at 13 weeks gestation revealed right-sided cardiac malformations resembling the clinical presentation of the family’s first child. Targeted DNA analysis showed that the fetus carried the same biallelic PLD1 variants as their older sibling. This case helps to further delineate the clinical spectrum of PLD1-related defects and highlights the value of both genome sequencing in congenital heart disease and early fetal echocardiography to establish phenotype. More >