Development of robotic sedation system

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K19588
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Review Section: Medium-sized Section 57:Oral science and related fields
• Research Institution: Tohoku University
• Principal Investigator: Mizuta Kentaro 東北大学, 歯学研究科, 教授 (40455796)
• Co-Investigator(Kenkyū-buntansha): 大町 真一郎 東北大学, 工学研究科, 教授 (30250856) ; 宮崎 智 東北大学, 工学研究科, 助教 (10755101) ; 飯島 毅彦 昭和大学, 歯学部, 客員教授 (10193129) ; 星島 宏 東北大学, 歯学研究科, 准教授 (90536781)
• Project Period (FY): 2021-07-09 – 2024-03-31
• Keywords: セデーション / 人工知能

Outline of Final Research Achievements

We have developed an artificial intelligence-assisted sedation system that uses deep learning of patient data and sedative drug doses over time, together with teacher data, to automatically control sedative drug doses and timing of administration according to patient characteristics. Specifically, by combining two machine learning models (sedative drug dose estimation model and sedative depth estimation model), we developed a recursive algorithm in which the artificial intelligence automatically predicts and controls the sedative drug dose according to the patient's sedation depth and a closed-loop, fully automatic sedation system that automatically controls the sedative drug administration rate. The results confirmed that future prediction of sedative drug dosage was achieved with high accuracy.

Free Research Field

麻酔科学

Academic Significance and Societal Importance of the Research Achievements

医療現場における鎮静法（セデーション）の利用件数は増加の一途を辿っており、歯科治療、消化管内視鏡検査、MRI検査、CT検査、小手術時に広く利用されている。これまで麻酔科医が「経験則」で行ってきた鎮静深度の調節作業を、「分析力」と「予測力」を兼ね備えた人工知能に置き換えることで、鎮静システム全体を自動化できることが期待される。