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[期刊论文]

High-content imaging and deep learning-driven detection of infectious bacteria in wounds

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author:

Zhang, Ziyi (Zhang, Ziyi.) [1] | Gao, Lanmei (Gao, Lanmei.) [2] | Zheng, Houbing (Zheng, Houbing.) [3] | Unfold

Indexed by:

EI Scopus SCIE

Abstract:

Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-driven framework that detects and classifies four bacteria commonly found in wounds: Acinetobacter baumannii (AB), Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). This framework leverages the pretrained ResNet50 deep learning architecture, trained on manually collected periodic bacterial colony-growth images from high-content imaging. In in vitro samples, our method achieves a detection rate of over 95% for early colonies cultured for 8 h, reducing detection time by more than 12 h compared to traditional Environmental Protection Agency (EPA)-approved methods. For colony classification, it identifies AB, EC, PA, and SA colonies with accuracies of 96%, 97%, 96%, and 98%, respectively. For mixed bacterial samples, it identifies colonies with 95% accuracy and classifies them with 93% precision. In mouse wound samples, the method identifies over 90% of developing bacterial colonies and classifies colony types with an average accuracy of over 94%. These results highlight the framework's potential for improving the clinical treatment of wound infections. Besides, the framework provides the detection results with key feature visualization, which enhance the prediction credibility for users. To summarize, the proposed framework enables high-throughput identification, significantly reducing detection time and providing a cost-effective tool for early bacterial detection.

Keyword:

Bacterial detection Deep learning High-content imaging Wound infection

Community:

  • [ 1 ] [Zhang, Ziyi]Fuzhou Univ, Coll Comp & Data Sci, Coll Software, Fuzhou, Fujian, Peoples R China
  • [ 2 ] [Li, Gaozheng]Fuzhou Univ, Coll Comp & Data Sci, Coll Software, Fuzhou, Fujian, Peoples R China
  • [ 3 ] [Ye, Zhaoting]Fuzhou Univ, Coll Comp & Data Sci, Coll Software, Fuzhou, Fujian, Peoples R China
  • [ 4 ] [Weng, Zuquan]Fuzhou Univ, Coll Comp & Data Sci, Coll Software, Fuzhou, Fujian, Peoples R China
  • [ 5 ] [Gao, Lanmei]Fuzhou Univ, Coll Biol Sci & Engn, Fuzhou, Fujian, Peoples R China
  • [ 6 ] [Zhong, Yi]Fuzhou Univ, Coll Biol Sci & Engn, Fuzhou, Fujian, Peoples R China
  • [ 7 ] [Sun, Qi]Fuzhou Univ, Coll Biol Sci & Engn, Fuzhou, Fujian, Peoples R China
  • [ 8 ] [Weng, Zuquan]Fuzhou Univ, Coll Biol Sci & Engn, Fuzhou, Fujian, Peoples R China
  • [ 9 ] [Zheng, Houbing]Fujian Med Univ, Dept Plast & Cosmet Surg, Affiliated Hosp 1, Fuzhou, Fujian, Peoples R China
  • [ 10 ] [Wang, Biao]Fujian Med Univ, Dept Plast & Cosmet Surg, Affiliated Hosp 1, Fuzhou, Fujian, Peoples R China
  • [ 11 ] [Weng, Zuquan]Fujian Med Univ, Dept Plast & Cosmet Surg, Affiliated Hosp 1, Fuzhou, Fujian, Peoples R China

Reprint 's Address:

  • 翁祖铨

    [Weng, Zuquan]Fuzhou Univ, Coll Comp & Data Sci, Coll Software, Fuzhou, Fujian, Peoples R China;;[Weng, Zuquan]Fuzhou Univ, Coll Biol Sci & Engn, Fuzhou, Fujian, Peoples R China;;[Weng, Zuquan]Fujian Med Univ, Dept Plast & Cosmet Surg, Affiliated Hosp 1, Fuzhou, Fujian, Peoples R China

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Source :

BIOPROCESS AND BIOSYSTEMS ENGINEERING

ISSN: 1615-7591

Year: 2024

Issue: 2

Volume: 48

Page: 301-315

3 . 5 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

30 Days PV: 0

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