Xinggong Liang , Gongji Wang , Zhengyang Zhu , Wanqing Zhang , Yuqian Li , Jianliang Luo , Han Wang , Shuo Wu , Run Chen , Mingyan Deng , Hao Wu , Chen Shen , Gengwang Hu , Kai Zhang , Qinru Sun , Zhenyuan Wang
{"title":"利用病理图像和人工智能算法识别不同温度下分解阶段的细菌感染类型","authors":"Xinggong Liang , Gongji Wang , Zhengyang Zhu , Wanqing Zhang , Yuqian Li , Jianliang Luo , Han Wang , Shuo Wu , Run Chen , Mingyan Deng , Hao Wu , Chen Shen , Gengwang Hu , Kai Zhang , Qinru Sun , Zhenyuan Wang","doi":"10.1016/j.mimet.2025.107180","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":16409,"journal":{"name":"Journal of microbiological methods","volume":"236 ","pages":"Article 107180"},"PeriodicalIF":1.9000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of bacterial infection types in decomposition stages at various temperatures using pathology images and artificial intelligence algorithms\",\"authors\":\"Xinggong Liang , Gongji Wang , Zhengyang Zhu , Wanqing Zhang , Yuqian Li , Jianliang Luo , Han Wang , Shuo Wu , Run Chen , Mingyan Deng , Hao Wu , Chen Shen , Gengwang Hu , Kai Zhang , Qinru Sun , Zhenyuan Wang\",\"doi\":\"10.1016/j.mimet.2025.107180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":16409,\"journal\":{\"name\":\"Journal of microbiological methods\",\"volume\":\"236 \",\"pages\":\"Article 107180\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of microbiological methods\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016770122500096X\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of microbiological methods","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016770122500096X","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Identification of bacterial infection types in decomposition stages at various temperatures using pathology images and artificial intelligence algorithms
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.
期刊介绍:
The Journal of Microbiological Methods publishes scholarly and original articles, notes and review articles. These articles must include novel and/or state-of-the-art methods, or significant improvements to existing methods. Novel and innovative applications of current methods that are validated and useful will also be published. JMM strives for scholarship, innovation and excellence. This demands scientific rigour, the best available methods and technologies, correctly replicated experiments/tests, the inclusion of proper controls, calibrations, and the correct statistical analysis. The presentation of the data must support the interpretation of the method/approach.
All aspects of microbiology are covered, except virology. These include agricultural microbiology, applied and environmental microbiology, bioassays, bioinformatics, biotechnology, biochemical microbiology, clinical microbiology, diagnostics, food monitoring and quality control microbiology, microbial genetics and genomics, geomicrobiology, microbiome methods regardless of habitat, high through-put sequencing methods and analysis, microbial pathogenesis and host responses, metabolomics, metagenomics, metaproteomics, microbial ecology and diversity, microbial physiology, microbial ultra-structure, microscopic and imaging methods, molecular microbiology, mycology, novel mathematical microbiology and modelling, parasitology, plant-microbe interactions, protein markers/profiles, proteomics, pyrosequencing, public health microbiology, radioisotopes applied to microbiology, robotics applied to microbiological methods,rumen microbiology, microbiological methods for space missions and extreme environments, sampling methods and samplers, soil and sediment microbiology, transcriptomics, veterinary microbiology, sero-diagnostics and typing/identification.