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

Xu, J. et al. (2023) Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy. Frontiers in Microbiology, 14, 1125676. (doi: 10.3389/fmicb.2023.1125676) (PMID:37032865) (PMCID:PMC10073597)

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. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

• Item Type: Articles
• Additional Information: This work was supported by Innovate UK AMRAR project (File reference 104984), National Key R&D Program of China (MOST, 2018YFE0101800), and international collaboration project between University of Oxford and Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences. The authors thank finance and instrumentation support from EPSRC (EP/M002403/1 and EP/M02833X/1). National High Level Hospital Clinical Research Funding 2022-PUMCH-B-028 and 2022-PUMCH-C-060.
• Keywords: Raman spectoscopy, single cell, fungal diagnosis, clinical diagnosis, artificial intelligence.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yin, Professor Huabing
• Authors: Xu, J., Luo, Y., Wang, J., Tu, W., Yi, X., Xu, X., Song, Y., Tang, Y., Hua, X., Yu, Y., Yin, H., Yang, Q., and Huang, W. E.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: Frontiers in Microbiology
• Publisher: Frontiers Media
• ISSN: 1664-302X
• ISSN (Online): 1664-302X
• Copyright Holders: Copyright © 2023 Xu, Luo, Wang, Tu, Yi, Xu, Song, Tang, Hua, Yu, Yin, Yang and Huang
• First Published: First published in Frontiers in Microbiology 14: 1125676
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.6084/m9.figshare.21081931.v1

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304354		An integrated microfluidic single cell Raman technology for rapid diagnosis of pathogens and their antibiotic resistance	Huabing Yin	Innovate UK (INNOVATE)	104984	ENG - Biomedical Engineering