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

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-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 beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-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 beta-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.

Automated summary

Beta-lactamase is a major cause of antimicrobial resistance and the development of a rapid and cost-effective point-of-care testing (POCT) method is urgently needed. However, current POCT methods often suffer from inaccuracies due to volatile environmental factors. This study presents an artificial intelligence (AI)-assisted mobile health system that utilizes a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. This smart system successfully calibrates the temperature and pH in beta-lactamase level detection, demonstrating its problem-solving ability in interdisciplinary research and potential clinical benefits.