- 著者
- Kiyoyuki Chinzei Akinobu Shimizu Kensaku Mori Kanako Harada Hideaki Takeda Makoto Hashizume Mayumi Ishizuka Nobumasa Kato Ryuzo Kawamori Shunei Kyo Kyosuke Nagata Takashi Yamane Ichiro Sakuma Kazuhiko Ohe Mamoru Mitsuishi
- 出版者
- Japanese Society for Medical and Biological Engineering
- 雑誌
- Advanced Biomedical Engineering (ISSN:21875219)
- 巻号頁・発行日
- vol.7, pp.118-123, 2018 (Released:2018-05-24)
- 参考文献数
- 3
- 被引用文献数
- 30
AI-based medical and healthcare devices and systems have unique characteristics including 1) plasticity causing changes in system performance through learning, and need of creating new concepts about the timing of learning and assignment of responsibilities for risk management; 2) unpredictability of system behavior in response to unknown inputs due to the black box characteristics precluding deductive output prediction; and 3) need of assuring the characteristics of datasets to be used for learning and evaluation. The Subcommittee on Artificial Intelligence and its Applications in Medical Field of the Science Board, the Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan, examined “new elements specific to AI” not included in conventional technologies, thereby clarifying the characteristics and risks of AI-based technologies. This paper summarizes the characteristics and clinical positioning of AI medical systems and their applications from the viewpoint of regulatory science, and presents the issues related to the characteristics and reliability of data sets in machine learning.
- 著者
- Ritsuko Yamamoto-Honda Yoshihiko Takahashi Yasumichi Mori Shigeo Yamashita Yoko Yoshida Shoji Kawazu Yasuhiko Iwamoto Hiroshi Kajio Hidekatsu Yanai Shuichi Mishima Nobuhiro Handa Kotaro Shimokawa Akiko Yoshida Hiroki Watanabe Kazuhiko Ohe Takuro Shimbo Mitsuhiko Noda
- 出版者
- (社)日本内分泌学会
- 雑誌
- Endocrine Journal (ISSN:09188959)
- 巻号頁・発行日
- pp.EJ16-0521, (Released:2017-03-18)
- 被引用文献数
- 6
Type 2 diabetes, which is characterized by a combination of decreased insulin secretion and decreased insulin sensitivity, can be delayed or prevented by healthy lifestyle behaviors. Therefore, it is important that the population in general understands their personal risk at an early age to reduce their chances of ever developing the disease. A family history of hypertension is known to be associated with insulin resistance, but the effect of a family history of hypertension on the onset of type 2 diabetes has not well been examined. We performed a retrospective study examining patient age at the time of the diagnosis of type 2 diabetes by analyzing a dataset of 1,299 patients (1,021 men and 278 women) who had been diagnosed as having type 2 diabetes during a health checkup. The mean ± standard deviation of the patient age at the time of the diagnosis of diabetes was 49.1 ± 10.4 years for patients with a family history of hypertension and 51.8 ± 11.4 years for patients without a family history of hypertension (p < 0.001). A multivariate linear regression analysis showed a significant association between a family history of hypertension and a younger age at the time of the diagnosis of type 2 diabetes, independent of a family history of diabetes mellitus and a male sex, suggesting that a positive family history of hypertension might be associated with the accelerated onset of type 2 diabetes.
- 著者
- Ichiro Takeuchi Hideo Fujita Kazuhiko Ohe Ryuta Imaki Nobuhiro Sato Kazui Soma Shinichi Niwano Tohru Izumi
- 出版者
- 一般社団法人 インターナショナル・ハート・ジャーナル刊行会
- 雑誌
- International Heart Journal (ISSN:13492365)
- 巻号頁・発行日
- vol.54, no.1, pp.45-47, 2013 (Released:2013-02-20)
- 参考文献数
- 13
- 被引用文献数
- 3 10
It is important for myocardial infarction patients to undergo immediate reperfusion of the affected coronary artery. In order to improve the prognosis, efforts to shorten the door to balloon time to within 90 minutes have been made. However, conventional methods such as faxing electrocardiograms (ECG) have not become widespread due to their high cost and lack of sharpness of the ECG. The “Doctor Car” (rapid response car system) of Kitasato University Hospital is now equipped with a Mobile Cloud ECG system. With this system, 12-lead ECG data obtained in the field are transmitted to the cloud server via a standard mobile telephone network. Since it uses an existing phone network, the cost of this system is low and it is fairly reliable. Cardiologists at the hospital read the ECG waveforms on the cloud server and decide whether emergency cardiac catheterization is necessary. In our fi rst case using this Mobile Cloud ECG system, the door to balloon time could be shortened.