AI-assisted Cervical Cancer Diagnosis System
About it
The LBC AI-Assisted Diagnostic System is designed for liquid-based cervical cytology.
It automatically classifies slides, detects abnormal cells, and generates reports compliant with The Bethesda System (TBS) standards.
Clinically validated on over one million samples, it reduces pathologists’ workload, prevents missed diagnoses, and supports large-scale cervical cancer screening and research.
TBS-Based Lesion Classification
Detects and grades cervical lesions according to The Bethesda System (TBS), covering a full spectrum of pathological changes.
Comprehensive Infection Detection
Simultaneously identifies Trichomonas, fungi, Actinomyces, and other microbial infections for more complete diagnostic insight.
Risk-Prioritized Review
Automatically ranks slides by lesion severity, allowing pathologists to focus on high-risk areas and improve reading efficiency.
High Clinical Compatibility
Supports multiple sample preparation methods, including membrane, sedimentation, and centrifugation techniques.
Flexible Format Adaptation
Compatible with slide formats and image outputs from various manufacturers and scanner systems.
Certified & Compliant
The first pathology AI in China with a Class II Medical Device Certificate, fully compliant with medical data standards and integrable with hospital LIS systems.
Recognizable Lesion and Infection Types
| Category | Detected Types | Description |
| Epithelial Lesions | HSIL, ASC-H, LSIL, ASC-US | Covers the full range of squamous intraepithelial abnormalities based on The Bethesda System (TBS). |
| Glandular Lesions | AGC-NOS, Endocervical glandular cells | Detects abnormal glandular changes including atypical endocervical or endometrial cells. |
| Microbial Infections | Trichomonas, Candida, Clue cells, Actinomyces, Herpes simplex virus (HSV) | Identifies common infectious agents and inflammatory indicators in cervical cytology. |
Powering global digital pathology with performance, scale, and trust
The Numbers Behind Our Innovation
Network and GPU Server Requirements
| Parameter Category | Minimum Requirement | Recommended Configuration | Remarks |
| Local Network Bandwidth | Between server and client ≥100Mbps | Between server and client ≥1Gbps (preferably optical) | Core switch supports ≥10Gbps uplink |
| Network Latency | ≤50ms (between LAN nodes) | ≤20ms (between LAN nodes) | Supports link aggregation (LACP) for improved stability |
| Network Topology | Supports star/tree topology | Supports redundancy (dual-core switch) | Key nodes (e.g., database server) require dual-network binding |
| Interface Type | Client RJ45 (10/100BASE-T) | Server RJ45 (1000BASE-T) + Optical port (optional) | Optical interface supports LC/SC connectors, transmission distance ≤5km |
| Parameter Category | Minimum Requirement | High-Performance Configuration (e.g., AI Training / Rendering) | Remarks |
| GPU Model | NVIDIA 2080Ti ×1 | NVIDIA A4000 ×1 | |
| GPU Memory | 11GB GDDR6 (ECC optional) | 16GB HBM2e (ECC required) | |
| GPU Quantity | Single GPU per host | Single GPU per host | |
| CPU | Intel Xeon E-2274G (4 cores, 8 threads, 3.8GHz) | Intel Xeon E-2274G (8 cores, 16 threads, 3.8GHz) | Main frequency ≥3.0GHz, cache ≥24MB |
| Memory | 32GB DDR4-2666 (ECC) | 64GB DDR5-4800 (ECC Registered) | |
| Storage Interface | 1×NVMe SSD (PCIe 3.0) | 2×NVMe SSD (PCIe 4.0, RAID 0) | Supports hot-swap, continuous read/write ≥3000MB/s |
| Power & Cooling | 900W 80+ Gold PSU, air cooling | 1600W 80+ Platinum PSU, liquid cooling system | |
| OS Compatibility | Supports Ubuntu 22.04 server version | Supports Ubuntu 22.04 server version | Docker pre-installed (ready for container deployment) |
