その他

Department of Preemptive Cardiovascular Medicine先制循環器医療学寄附講座

  • Preemptive medicine
  • Artificial intelligence
  • Prediction
  • Cardiac implantable electronic devices (CIEDs)
  • 心不全
  • 心房細動
  • 致死性不整脈
  • 遠隔医療

STAFF

Professor

  • Satoshi, YasudaProfessor. 安田 聡 教授 (兼任)

Other Faculty / Staff

  • Takashi Noda
    Lect. 野田 崇 講師(兼務)
  • Hiroyuki Sato
    Research Associate. 佐藤 宏行 助手

CONTACT

TEL:+81-22-717-7152
E-MAIL:dcvm*cardio.med.tohoku.ac.jp
(「*」を「@」に変換してください)

OUTLINE

We are now facing an unprecedented aging society, and a key task is to achieve "extension of healthy life expectancy" for the elderly. For the purpose, it is considered important to prevent stroke and cardiovascular disease preemptively. Atrial fibrillation as an exacerbating factor of heart failure, an approach to fatal arrhythmia related to sudden cardiac death, and the link between heart failure and arrhythmia are attracting the most attention at this moment. In the laboratory of Department of Preemptive Cardiovascular Medicine, we are elucidating the pathophysiology of heart failure and arrhythmia by performing artificial intelligence (AI) analysis using methods such as machine learning and deep learning. After identifying risk factors that should be intervened, we will also develop clinically applicable algorithms and new therapeutic devices in the field of heart failure and arrhythmia, especially regarding the link between heart failure and arrhythmia, as well as creating novel evidences

未曾有の高齢化社会を迎えた現在、高齢者における「健康寿命の延伸」を実現することが、喫緊の課題です。そのためには脳卒中と循環器病の抑制が重要と考えられ、心不全の増悪因子としての心房細動や、突然死に関係する致死性不整脈に対するアプローチ、心不全と不整脈との連関が注目されています。心不全と不整脈、それぞれの病態解明に向けて、機械学習やディープラーニングなどの手法を用いた人工知能(AI)解析を行うことで、介入すべきリスクファクターの同定やこれらの基盤的知見をもとに臨床応用可能なアルゴリズムや新規治療機器の開発、心不全と不整脈の連関に関する新たなエビデンスの創出を行っています。

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