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RISS 인기검색어
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- 저자 : Qinghua Zhong (검색결과 3 건)
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Yi Lu,Xianhua Zhuo,Qinghua Zhong,Jiachen Sun,Chujun Li,Min Zhi 대한초음파의학회 2023 ULTRASONOGRAPHY Vol.42 No.1
Purpose: Models for predicting perforation during endoscopic resection (ER) of gastric submucosal tumors (SMTs) originating from the muscularis propria (MP) are rare. Therefore, this study was conducted to determine important parameters in endoscopic ultrasonography (EUS) images to predict perforation and to build predictive models. Methods: Consecutive patients with gastric SMTs originating from the MP who received ER from May 1, 2013 to January 15, 2021 were retrospectively reviewed. They were classified into case and control groups based on the presence of perforation. Logistic multivariate analysis was used to identify potential variables and build predictive models (models 1 and 2: with and without information on tumor pathology, respectively). Results: In total, 199 EUS procedures (194 patients) were finally chosen, with 99 procedures in the case group and 100 in the control group. The ratio of the inner distance to the outer distance (I/O ratio) was significantly larger in the case group than in the control group (median ratio, 2.20 vs. 1.53; P<0.001). Multivariate analysis showed that age (odds ratio [OR], 1.036 in model 1; OR, 1.046 in model 2), the I/O ratio (OR, 2.731 in model 1; OR, 2.372 in model 2), and the pathology of the tumors (OR, 10.977 for gastrointestinal stromal tumors; OR, 15.051 for others in model 1) were risk factors for perforation. The two models to predict perforation had areas under the curve of 0.836 (model 1) and 0.755 (model 2). Conclusion: EUS was useful in predicting perforation in ER for gastric SMTs originating from the MP. Two predictive models were developed.
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Multi-rate Dynamic Event-triggered H∞ Control for Linear Singularly Perturbed Systems
Shixian Luo,Zhan He,Xin Chen,Qinghua Zhong 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.11
This paper addresses the event-triggered multi-rate H∞ control problem for linear singularly perturbed systems. A two-time-scale dynamic event-triggering mechanism is proposed to generate sampling time sequences for slow and fast subsystems. The proposed dynamic event-triggering mechanism embeds with internal dynamic variables and dwell times, which can not only increase the inter-event times compared with its static counterparts but also avoid Zeno behavior naturally. The multi-rate sampled-data control law is designed by discretizing the continuous-time composite controller based on the reduced-order technique. By employing switched time-delay system approach combined with a novel switching time-dependent Lyapunov functional, sufficient conditions for stability and L2-gain properties of the closed-loop systems are established in terms of linear matrix inequalities. The effectiveness of the theoretical results is demonstrated by a vehicle active suspension system.
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Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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