ArticleFrontiers in microbiology2023
Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Deep Learning-Enhanced Raman Microspectroscopy Enables Rapid Microbial Classification and Captures Phylogenetic Relationships.Microorganisms · 2026Article
- Review of the Microbial Spectrum of Mixed Respiratory Fungal Infections.Journal of epidemiology and global health · 2026Review
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Molecular Identification and RNA-Based Management of Fungal Plant Pathogens: From PCR to CRISPR/Cas9.International journal of molecular sciences · 2026Review
- The structural language of fungal cell wall sugars in WHO priority fungi: the determinant of pathogenicity and the gateway to new therapies.Frontiers in immunology · 2026Review
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy.Nature communications · 2025Article
- Identification of a Fusobacterial RNA-binding protein involved in host small RNA-mediated growth inhibition.International journal of oral science · 2025Article
- Random splicing assisted deep learning for breast cancer cell line classification via Raman spectroscopy.Computational and structural biotechnology journal · 2025Article
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
- Article
- Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data.ISME communications · 2025Article
- Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens.Foods (Basel, Switzerland) · 2024Article
- Episymbiotic Saccharibacteria induce intracellular lipid droplet production in their host bacteria.The ISME journal · 2024Article
- Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases.Frontiers in microbiology · 2024Review
Corrections and comments
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Authors and funding
13 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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