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Endocuff-Assisted versus Cap-Assisted Colonoscopy Performed by Trainees: A Retrospective Study
Yutaka Okagawa,Tetsuya Sumiyoshi,Yusuke Tomita,Shutaro Oiwa,Fumihiro Ogata,Takashi Jin,Masahiro Yoshida,Ryoji Fujii,Takeyoshi Minagawa,Kohtaro Morita,Hideyuki Ihara,Michiaki Hirayama,Hitoshi Kondo 대한소화기내시경학회 2020 Clinical Endoscopy Vol.53 No.3
Background/Aims: The adenoma detection rate (ADR) of screening colonoscopies performed by trainees is often lower than thatof colonoscopies performed by experts. The effcacy of cap-assisted colonoscopy (CAC) in adenoma detection is well documented,especially that of CACs performed by trainees. Endocuff, a new endoscopic cap, is reportedly useful for adenoma detection; however,no trials have compared the effcacy of Endocuff-assisted colonoscopy (EAC) and CAC conducted by trainees. Therefore, the presentstudy retrospectively compared the effcacy between EAC and CAC in trainees. Methods: This was a single-center, retrospective study involving 305 patients who underwent either EAC or CAC performed by threetrainees between January and December 2018. We evaluated the ADR, mean number of adenomas detected per patient (MAP), cecalintubation rate, cecal intubation time, and occurrence of complications between the EAC and CAC groups. Results: The ADR was significantly higher in the EAC group than in the CAC group (54.3% vs. 37.3%, p=0.019), as was the MAP (1.36vs. 0.74, p=0.003). No significant differences were found between the groups with respect to the cecal intubation rate or cecal intubationtime. No major complications occurred in either group. Conclusions: Our results suggest that EAC exhibits increased ADR and MAP compared to CAC when performed by trainees.
A New Estimation Method by Utilizing On-Line Tracking Simulator
Makoto Nakaya,Akio Nakabayashi,Tetsuya Ohtani,Yoshihiro Ikegaya,Satoshi Asawa,Nobuhiko Terashima,Yuji Izawa,Marina Gravina Ogata 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In the chemical process, it is hard to describe the polymer property by a rigorous model based on the physical and chemical theory. The soft sensor with the statistical data processing is often used for modeling to monitor and to control the polymer quality. We confirmed regarding the estimation accuracy that using data from the on-line tracking simulator is better than using data from the conventional use of the soft sensor. The on-line tracking simulatorperfectly simulates the target and provides the virtual input data which can not be measured by the soft sensor on the real time. We propose the application of the rigorous and the statistical model to the plant operation.
Murata, Shingo,Yamashita, Yuichi,Arie, Hiroaki,Ogata, Tetsuya,Sugano, Shigeki,Tani, Jun IEEE 2017 IEEE transactions on neural networks and learning Vol.28 No.4
<P>We suggest that different behavior generation schemes, such as sensory reflex behavior and intentional proactive behavior, can be developed by a newly proposed dynamic neural network model, named stochastic multiple timescale recurrent neural network (S-MTRNN). The model learns to predict subsequent sensory inputs, generating both their means and their uncertainty levels in terms of variance (or inverse precision) by utilizing its multiple timescale property. This model was employed in robotics learning experiments in which one robot controlled by the S-MTRNN was required to interact with another robot under the condition of uncertainty about the other's behavior. The experimental results show that self-organized and sensory reflex behavior-based on probabilistic prediction-emerges when learning proceeds without a precise specification of initial conditions. In contrast, intentional proactive behavior with deterministic predictions emerges when precise initial conditions are available. The results also showed that, in situations where unanticipated behavior of the other robot was perceived, the behavioral context was revised adequately by adaptation of the internal neural dynamics to respond to sensory inputs during sensory reflex behavior generation. On the other hand, during intentional proactive behavior generation, an error regression scheme by which the internal neural activity was modified in the direction of minimizing prediction errors was needed for adequately revising the behavioral context. These results indicate that two different ways of treating uncertainty about perceptual events in learning, namely, probabilistic modeling and deterministic modeling, contribute to the development of different dynamic neuronal structures governing the two types of behavior generation schemes.</P>