An AI-powered sensor system for rapid bacteria detection and identification

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K14581
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 28050:Nano/micro-systems-related
• Research Institution: Nagoya University
• Principal Investigator: Taisuke Shimada 名古屋大学, 工学研究科, 助教 (00850140)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: 細菌 / 学習型AIセンサシステム / 単一細菌センシング / ポアセンサ / 機械学習

Outline of Final Research Achievements

Bacteria infections are one of leading causes of death in the world, and emerging drug-resistant bacteria has increased its threat. Sensors using micro- and nano-structures are technical candidates that can realize rapid bacterial detection and identification; however they require a large number of receptor molecules (e.g., peptides and antibodies) to cover a wide variety of target bacteria. This work has proposed an AI-powered sensor system without using receptors for rapid bacterial detection and identification. The sensor system enabled to detect and identify various bacteria rapidly via single bacteria sensing and machine learning-driven pulse analyses.

Free Research Field

MicroTAS

Academic Significance and Societal Importance of the Research Achievements

単一細菌細胞の物理的な特性を反映したパルスセンシングを基軸とした、データ駆動的な細菌種の同定が可能な独自システムを開発する。本システムによりレセプターを必要としない学習型センサという新規概念を創造して、レセプターレスな細菌検出・同定技術を創出する。本研究成果は、レセプターに依存したセンサによる標的細菌の同定にパラダイムシフトを起こし、学習型センサによる迅速・網羅的検出とデータに基づく同定への大転換をもたらす。