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<p>Bloodstream infections (BSIs) are life-threatening conditions with rising mortality, urgently requiring rapid pathogen identification to guide timely antibiotic therapy. Current methods (phenotypic identification, MALDI-TOF MS, molecular techniques) are faced with limitations in cost, turnaround time and accessibility. In this study, it was discovered that the drying of microbial suspensions on slides could lead to species-specific desiccation patterns, which reflects the physicochemical properties and complex interactions of microorganisms during evaporation. Inspired by this interesting phenomenon, an AI-powered platform is proposed for rapid pathogen identification through automated analysis of these patterns. By establishing an image dataset comprising 10,055 desiccation patterns of common BSIs pathogens (<i>Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, Enterococcus faecium</i> and <i>Candida albicans</i>), a ResNet-34 deep learning model is trained, achieving a classification accuracy of 91.8% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.99. Following pure culture, identification is completed within 5&#xa0;min after a simple drying step at 40&#xa0;°C. With its minimal sample requirement, low cost, and operational simplicity, this platform demonstrates significant potential as a point-of-care diagnostic tool, particularly in resource-constrained regions. This technology offers a promising strategy to combat BSIs and improve patient outcomes.</p> Graphical Abstract <p></p>

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Deep learning-driven morphological fingerprinting: rapid, accurate and low-cost pathogen identification via the analysis of dried patterns of droplets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Shujuan Guan,
  • Ruyue Yang,
  • Danning Deng,
  • Huilin Long,
  • Zihao Ou,
  • Xiaoxue Ge,
  • Xiujuan Jiang,
  • Xiumei Hu,
  • Dingqiang Chen

摘要

Bloodstream infections (BSIs) are life-threatening conditions with rising mortality, urgently requiring rapid pathogen identification to guide timely antibiotic therapy. Current methods (phenotypic identification, MALDI-TOF MS, molecular techniques) are faced with limitations in cost, turnaround time and accessibility. In this study, it was discovered that the drying of microbial suspensions on slides could lead to species-specific desiccation patterns, which reflects the physicochemical properties and complex interactions of microorganisms during evaporation. Inspired by this interesting phenomenon, an AI-powered platform is proposed for rapid pathogen identification through automated analysis of these patterns. By establishing an image dataset comprising 10,055 desiccation patterns of common BSIs pathogens (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, Enterococcus faecium and Candida albicans), a ResNet-34 deep learning model is trained, achieving a classification accuracy of 91.8% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.99. Following pure culture, identification is completed within 5 min after a simple drying step at 40 °C. With its minimal sample requirement, low cost, and operational simplicity, this platform demonstrates significant potential as a point-of-care diagnostic tool, particularly in resource-constrained regions. This technology offers a promising strategy to combat BSIs and improve patient outcomes.

Graphical Abstract