95%
SFN/BFN sensitivity
Micro Solutions / Life Science
AI module for analysis of thyroid cytology KFBio LBC Thyroid
AI analysis of thyroid cytology: 95% accuracy for differentiating SFN and BFN
Product overview
- KFBio AI — LBC Thyroid
- KFBio
- China
- KFBio AI Models
- LBC Thyroid
AI analysis of thyroid cytology: 95% accuracy for differentiating SFN and BFN
Structured Nuclear Signature Report in Less Than a Minute
The AI module for pre-analysis of fine-needle aspiration thyroid cytology helps the cytologist to distinguish between benign, borderline and malignant changes faster and shows explainable areas of interest on the drug.
The module is designed for handheld and liquid FNA drugs, including those with sparse and overlapping cellular material, it recognizes papillary cancer, suspected malignancy, follicular neoplasms (SFN), atypia of unknown value (AUS), and benign nodes, marks nuclear grooves and matte glass nuclei, and forms Structured report in less than a minute.
According to the manufacturer, the sensitivity and accuracy of differentiation of SFN and BFN is 95%, the drug field coverage is 100%; the model is trained on more than 10,000 biopsy-confirmed slides. Local installation requires a network of 100 Mbit / s, NVIDIA 2080 Ti GPU and 32 GB of RAM on the Ubuntu 22.04 server; the recommended configuration is Gigabit network, NVIDIA A4000 and 64 GB of RAM.
Key figures
95%
precision
100%
field-covering
>10 000
learning-slide
Applications
- Fine needle aspiration cytology
- Diagnosis of thyroid nodes
- Oncocytology
Models and configurations
- LBC Thyroid
Product details
AI analysis of thyroid cytology
Distinguish between SFN and BFN
Analysis opportunities
- Optimization for handheld and liquid FNA drugs, including sparse and overlapping cells
- Recognition of papillary cancer, suspected malignancy, SFN, AUS and benign follicular nodes
- Isolation of nuclear grooves and matte glass nuclei
- Structured report in less than a minute
precision
- 95% sensitivity and 95% accuracy of differentiation between SFN and BFN
- 100% coverage of the drug field
- Training on more than 10,000 biopsy-confirmed slides
Infrastructure
- Local installation: network from 100 Mbit / s, NVIDIA 2080 Ti GPU, RAM from 32 GB, Ubuntu 22.04
- Recommended configuration: Gigabit network, NVIDIA A4000, 64 GB RAM
Diagnostics
| Identified changes | papillary cancer, suspected malignancy, SFN, AUS, benign follicular nodes |
|---|---|
| Time of reporting | less than 1 minute |
precision
| Sensitivity (SFN/BFN) | 95% |
|---|---|
| precision | 95% |
| Covering the drug field | 100% |
| Training sample | over 10,000 slide |
Infrastructure
| Minimum requirements | network from 100 Mbit / s, NVIDIA 2080 Ti GPU, RAM from 32 GB, Ubuntu 22.04 |
|---|---|
| Recommended configuration | Gigabit network, NVIDIA A4000 GPU, 64 GB RAM |
Related equipment types
- Digital slide scanners
- Digital slide management system
- FNA cytology equipment
- Staining machines for cytology
Related products
- KFBio KF-DSMS — Digital slide management system
- Gram Bioline ExGuard ER600W — FNA cytology equipment
- ELITech Aerospray Gram — Staining machines for cytology
Classification
- Histology and cytology
- AI analysis of thyroid fluid cytology
- Cytological equipment
- Screening and digital analysis
- Automated primary screening systems
- KFBio AI — LBC Thyroid