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Nanotechnology in artificial organ development and its application in diagnosis methodology in baroreflex sensitivity of patients with hypertension

100%

Yambe T. , Yoshizawa S. , Sugita N. , Tanaka A. , Imachi K.

Biocybernetics and Biomedical Engineering

2007

tom Vol. 27, no. 1-2

65-70

Space in the human body is so limited that nanotechnology and micromachining technology are the important for the development of the internal artificial organs. Based on the nanotechnology, various kinds of artificial organ development have been performed in Tohoku University including artificial myocardium, artificial heart, rotary blood pump, artificial esophagus, and artificial sphincter. Furthermore, automatic control algorithm for the artificial heart and assisted circulation was applied to the invention of the new diagnosis methodology for the baroreflex sensitivity of the patients with hypertension. Successful clinical application of this new invention was performed. The technical application to the large range is expectable in artificial internal-organs development.

Intelligent sensing and monitoring : respiratory motion prediction for tumor following radiotherapy

72%

Ichiji K. , Homma N. , Sakai M. , Bukovsky I. , Zhang X. , Osanai M. , Abe M. , Sugita N. , Yoshizawa M.

Journal of Artificial Intelligence and Soft Computing Research

2012

tom Vol. 2, No. 4

331--342

This paper presents a medical application of the intelligent sensing and monitoring, a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-varying periodical nature of lung tumor motion. Such estimation is achieved by using a novel multiple time-varying seasonal autoregressive (TVSAR) model in which several windows of different time-lengths are used to calculate correlation based fluctuation of periodic nature in the motion. The proposed method provides the prediction as a combination of those based on different window lengths. Multiple regression (MR), multilayer perceptron (MLP) and support vector regression (SVR) are used to combine and the prediction performances are evaluated by using clinical lung tumor motion. The proposed methods with the combined predictions showed high accurate prediction and are superior to the single different predictions. The average errors of MR, MLP, and SVR were 0.8455,0.8507, and 0.7530 mm at 0.5 s ahead, respectively. The results are clinically sufficient and thus clearly demonstrate that the proposed TVSAR with an appropriate combination method is useful for improving the prediction performance.