Artificial Intelligence
Last updated on
Jun 13, 2021
Modified from Molecular Cell 54, May 22, 2014.Artificial intelligence is a powerful tool to perform optimal prediction, using multiple inputs, for personalized medicine. In this era of Big Data, unified management and analysis of clinical data have been facilitated.
We are exploring methods and systems to integrate dynamic in vivo data from “living machines” into the unified management system of clinical data for more precise prediction and selection of optimal therapeutic strategies, by interconnecting “living machines” to digital devices.
Yasutomi Higashikuni, M.D., Ph.D., FESC
Assistant Professor of Cardiovascular and Genetic Research
My research interests include homeostatic inflammation, RNA metabolism and modification, and synthetic biology.
Related
- An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function
- Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis
- Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features
- Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning
- Deep learning model to detect significant aortic regurgitation using electrocardiography
Publications
Chest X-ray is one of the most widely used medical imaging tests worldwide to diagnose and manage heart and lung diseases. In this …
Hirotaka Ieki, Kaoru Ito, Mike Saji, Rei Kawakami, Yuji Nagatomo, Kaori Takada, Toshiya Kariyasu, Haruhiko Machida, Satoshi Koyama, Hiroki Yoshida, Ryo Kurosawa, Hiroshi Matsunaga, Kazuo Miyazawa, Kouichi Ozaki, Yoshihiro Onouchi, Susumu Katsushika, Ryo Matsuoka, Hiroki Shinohara, Toshihiro Yamaguchi, Satoshi Kodera, Yasutomi Higashikuni, M.D., Ph.D., FESC, Katsuhito Fujiu, Hiroshi Akazawa, Nobuo Iguchi, Mitsuaki Isobe, Tsutomu Yoshikawa, Issei Komuro
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of …
Shinnosuke Sawano, Satoshi Kodera, Masataka Sato, Susumu Katsushika, Issei Sukeda, Hirotoshi Takeuchi, Hiroki Shinohara, Atsushi Kobayashi, Hiroshi Takiguchi, Kazutoshi Hirose, Tatsuya Kamon, Akihito Saito, Hiroyuki Kiriyama, Mizuki Miura, Shun Minatsuki, Hironobu Kikuchi, Yasutomi Higashikuni, M.D., Ph.D., FESC, Norifumi Takeda, Katsuhito Fujiu, Jiro Ando, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro
Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection …
Takahiro Kokubo, Satoshi Kodera, Shinnosuke Sawano, Susumu Katsushika, Mitsuhiko Nakamoto, Hirotoshi Takeuchi, Nisei Kimura, Hiroki Shinohara, Ryo Matsuoka, Koki Nakanishi, Tomoko Nakao, Yasutomi Higashikuni, M.D., Ph.D., FESC, Norifumi Takeda, Katsuhito Fujiu, Masao Daimon, Hiroshi Akazawa, Hiroyuki Morita, Yutaka Matsuyama, Issei Komuro
Background, Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning …
Susumu Katsushika, Satoshi Kodera, Mitsuhiko Nakamoto, Kota Ninomiya, Nobutaka Kakuda, Hiroki Shinohara, Ryo Matsuoka, Hirotaka Ieki, Masae Uehara, Yasutomi Higashikuni, M.D., Ph.D., FESC, Koki Nakanishi, Tomoko Nakao, Norifumi Takeda, Katsuhito Fujiu, Masao Daimon, Jiro Ando, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro
Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that …
Susumu Katsushika, Satoshi Kodera, Mitsuhiko Nakamoto, Kota Ninomiya, Shunsuke Inoue, Shinnosuke Sawano, Nobutaka Kakuda, Hiroshi Taniguchi, Hiroki Shinohara, Ryo Matsuoka, Hirotaka Ieki, Yasutomi Higashikuni, M.D., Ph.D., FESC, Koki Nakanishi, Tomoko Nakao, Tomohisa Seki, Norifumi Takeda, Katsuhito Fujiu, Masao Daimon, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro
Events
The Present and the Future of Cardiovascular Medicine
Jun 3, 2020 11:00 PM — Jun 6, 2020 8:00 AM
Kobe, Online