Identification of bacterial infection types in decomposition stages at various temperatures using pathology images and artificial intelligence algorithms.

Journal: Journal of microbiological methods
Published Date: Jun 13, 2025

Abstract

Bacterial infections present a significant threat to human health, and accurate identification of infection type is crucial for both clinical and forensic applications. Although traditional diagnostic methods are reliable, they are often time-consuming, require specialized personnel and equipment, and have limited accessibility. Previous studies have demonstrated that pathology images combined with artificial intelligence (AI) algorithms can effectively classify bacterial infections in fresh tissue samples. In this study, we extend this approach to identify bacterial infection types in decomposed tissue under varying temperature conditions. Our findings indicate that decomposition factors, such as putrefaction and autolysis, do not impair model performance. The model exhibits strong classification efficacy across all tested temperatures (25 °C, 37 °C, and 4 °C), demonstrating robustness and generalizability. The overall area under the curve (AUC) values exceeded 0.920 and 0.820 at the patch and whole slide image (WSI) levels, respectively, in the training and testing sets, while surpassing 0.990 at the patch-level in the external validation set. These results confirm that AI-driven computational pathology can reliably distinguish bacterial infection types, even in decomposition states. Our method offers a novel approach for bacterial diagnosis in forensic pathology and supports infection prevention during autopsies.

Authors

  • Xinggong Liang
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Gongji Wang
    College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China.
  • Zhengyang Zhu
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Wanqing Zhang
    College of Food Science and Engineering, Northwest University, Xi'an 710069, China.
  • Yuqian Li
    School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: yuqianli@uestc.edu.cn.
  • Jianliang Luo
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Han Wang
    Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
  • Shuo Wu
    School of Chemistry, Dalian University of Technology, Dalian 116023, PR China. Electronic address: wushuo@dlut.edu.cn.
  • Run Chen
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Mingyan Deng
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Chen Shen
    Department of Foreign Languages, Xi'an Jiaotong University City College, Xi'an, China.
  • Gengwang Hu
    Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, People's Republic of China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Qinru Sun
    College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China.
  • Zhenyuan Wang
    Department of Forensic Pathology, College of Forensic Medicine, Xian Jiaotong University, Xi'an, Shaanxi, 710061, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals