9DA Solutions
Digital Pathology Platform
Upload, annotate and share WSIs. Centralize your digital pathology assets in one secure workflow.
Request DemoDiagnosis for Oral Squamous Cell Carcinoma
Our promising diagnosis app (LNTD) can classify patients into cancer case and non-cancer case, relieving the burden and stress of healthcare systems.
Technical Specs
- Recommendation for the presence of cancer on tissue images
- Two combined deep learning models for analysis, ResNet and LeNet
- Mainly support whole-slide brightfield images
Key Findings
- 98.6% Accuracy & 98.8% Confidence
- 100% of patients with oral cancer can be pathologically diagnosed
- Reduce pathologists’ workload
Prognosis Prediction for Oral Squamous Cell Carcinoma
Medical professionals can use our prognosis app (MILP) to monitor the post-treatment status of oral cancer patients, keeping them away from cancer to feel the true joy of life.
Technical Specs
- Recommendation for the probability of early vs. late/no recurrence
- Multiple instance learning (MIL) architecture for deep analysis
- Supports whole-slide brightfield and confocal images
- Interactive ROI (Region of Interest) selection within the tool
Key Findings
- 95% Accuracy & 94.4% Confidence
- 86% of patients with oral cancer recurrence can be detected
- Enables well-planned monitoring & treatment options
- Guides targeted therapy or immunotherapy decisions