Testing items
Morphological Detection Items
- White blood cells
- Red blood cells
- Epithelial cells
- Clue cells
- Vaginal Trichomonas
- Cocci
- Bacilli
- Fungi
- Pathogens
Additional Indicators
- Cleanliness
- Bacterial density
- Diversity
- Dominant bacteria
- Nugent score
- Donders score
Detection Principle
Gram Staining
- Simulates the manual staining process while automating classical Gram staining for vaginal samples.
- Ensures consistent and standardized staining results, improving microscopic evaluation quality.
- Streamlines laboratory workflow by reducing manual intervention and enhancing efficiency in sample processing.
Morphological Detection
- Uses artificial intelligence (AI) and machine vision technology to scan microscopic images and capture real-time visuals.
- Intelligently recognizes and classifies morphological components after Gram staining, ensuring precise identification.
- Facilitates fast and accurate evaluation of vaginal microecology, assisting in the diagnosis of microbial imbalances and infections.
Product feature detail diagram
Product Features
Automated Gram Staining and AI-Driven Cell Identificatio
Infiltration Staining Technology: Utilizes a columnar infiltration staining process to automate Gram staining, suspending the slide for efficient stain consumption. The resulting images feature a clear background, with distinct bacterial and cellular coloration, aiding identification and classification.
Position Tracking Technology: Scans at low magnification (20X) to locate small targets, calculating precise locations in densely packed areas. When switched to oil immersion (100X), the system quickly locates target areas using coordinate tracking, enabling magnification and multi-layered image acquisition, improving detection speed while ensuring no pathological components are missed.
Microscope Automated Control Technology: Combines precision motors with dedicated control algorithms, ensuring sub-micron movement precision. Paired with a high-resolution CCD imaging system, it enables high-precision focusing scans, ensuring fine structures of cells and bacteria are clearly presented.
Intelligent Analysis Technology: Based on convolutional neural networks (CNN) and deep learning methods, paired with intelligent image acquisition technology. By leveraging massive datasets and multi-layer iterative learning, a deep learning model for recognizing vaginal secretions has been developed. The model progressively extracts shallow, mid-level, and deep features, enhancing robustness and generalization capabilities.
Ensuring Accuracy in Microbial Diagnostics with AI-Powered Quality Control
Complies with Expert Consensus Requirements: Adheres to Gram staining guidelines, performing comprehensive analyses on bacterial community density, diversity, Nugent score, and Donders score, ensuring accurate diagnostic insights.
High Detection Accuracy: Effectively differentiates cocci, bacilli, streptococci, vibrios, and other bacterial types under 100x magnification, improving microbial ecological analysis. Bacteria are classified into Gram-positive (G+) and Gram-negative (G-) based on staining results.
Modular Flexibility: Features automatic staining and morphological analysis modules, which can be combined as needed to meet the diverse detection requirements of hospitals.
Quality Control System: Includes a dedicated cell positioning test program to assess system accuracy, ensuring high-quality hardware performance.
