Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.

ABSTRACT: Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. T

SUBMITTER: Xu J 

PROVIDER: S-EPMC10073597 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature