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Using pathology images and artificial intelligence to identify bacterial infections and their types
Xinggong Liang1, Gongji Wang2, Zhengyang Zhu1
1Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
Journal of Microbiological Methods
|April 15, 2025
Summary
Artificial intelligence (AI) analyzes pathology images to detect bacterial infections. This AI model accurately classifies infection types, offering a faster, more precise diagnostic tool for clinical use.
Area of Science:
- Pathology
- Computational Pathology
- Infectious Diseases
Background:
- Bacterial infections require rapid diagnosis for effective treatment.
- Traditional methods are slow; emerging technologies are costly.
- Pathology images offer a potential diagnostic resource.
Purpose of the Study:
- To develop an AI-powered method for diagnosing bacterial infections using pathology images.
- To classify different bacterial infection types computationally.
- To assess the accuracy and generalizability of the AI model.
Main Methods:
- Utilized pathology images for bacterial infection diagnosis.
- Applied artificial intelligence (AI) algorithms for image analysis.
- Classified infections at patch-level and whole slide image (WSI)-level.
Main Results:
- Microscopic examination confirmed bacterial presence without postmortem changes.
- AI model achieved an Area Under the Curve (AUC) > 0.950.
- High accuracy, robustness, and generalizability demonstrated across datasets.
Conclusions:
- AI analysis of pathology images can accurately identify bacterial infections.
- The AI model shows potential for distinguishing between bacterial infection types.
- This approach offers valuable technical support for faster, precise clinical diagnostics.
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