Artificial intelligence‐assisted point‐of‐care testing system for ultrafast and quantitative detection of drug‐resistant bacteria

Abstract

Abstract As one of the major causes of antimicrobial resistance, β‐lactamase develops rapidly among bacteria. Detection of β‐lactamase in an efficient and low‐cost point‐of‐care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)‐assisted mobile health system that consists of a paper‐based β‐lactamase fluorogenic probe analytical device and a smartphone‐based AI cloud. An ultrafast broad‐spectrum fluorogenic probe (B1) that could respond to β‐lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three‐dimensional microfluidic paper‐based analytical device was fabricated for integration of B1. Also, a smartphone‐based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the β‐lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem‐solving ability in interdisciplinary research, and demonstrates potential clinical benefits.

Keywords

• antimicrobial resistance
• artificial intelligence
• fluorogenic probe
• microfluidic sensors
• mobile health
• point‐of‐care testing