ArticleCells2022
A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images.
Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Non-destructive label-free automated identification of bacterial colonies at the species level directly on agar media using digital holography and convolutional neural network algorithms.Microbiology spectrum · 2026Article
- Multi-Class and Multi-Level Classification for Common Wound Bacteria Based on Reflectance Hyperspectral Imaging.Journal of biophotonics · 2026Article
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- Establishment and comparison ofFrontiers in nutrition · 2024Article
- Design and Validation of a Custom-Made Hyperspectral Microscope Imaging System for Biomedical Applications.Sensors (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious. Therefore, it is of great importance to automatically rapidly identify pathogenic bacteria in a short time. Here, we constructed an AI-assisted system for automating rapid bacteria genus identification, combining the hyperspectral microscopic technology and a deep-learning-based algorithm Buffer Net. After being trained and validated in the self-built dataset, which consists of 11 genera with over 130,000 hyperspectral images, the accuracy of the algorithm could achieve 94.9%, which outperformed 1D-CNN, 2D-CNN and 3D-ResNet. The AI-assisted system we developed has great potential in assisting clinicians in identifying pathogenic bacteria at the single-cell level with high accuracy in a cheap, rapid and automatic way. Since the AI-assisted system can identify the pathogenic genus rapidly (about 30 s per hyperspectral microscopic image) at the single-cell level, it can shorten the time or even eliminate the demand for cultivating. Additionally, the system is user-friendly for novices.
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Registered trials
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