Case Study - Cutting-edge Gastric Cancer Detection Initiative
Helix Genetics, in collaboration with leading Japanese researchers, is developing a convolutional neural network (CNN) to improve gastric cancer detection workflows and diagnostic support.
- Client
- Helix Genetics Internal Project
- Year
- Service
- AI Research and Development
Overview
Helix Genetics has embarked on a groundbreaking project to harness the power of convolutional neural networks for the detection of gastric cancer. This ongoing endeavor is in collaboration with a prestigious team of Japanese researchers who have previously demonstrated a CNN with an unprecedented 99% accuracy rate in identifying gastric cancer from imaging data. This collaboration underscores Helix Genetics' commitment to integrating cutting-edge AI technologies into healthcare to improve patient outcomes significantly.
The project's aim is to translate research outcomes into robust clinical tooling by improving model reliability, integration quality, and response time in real-world diagnostics environments.
What we did
- AI Research and Development
- Collaboration with Japanese Research Teams
- Convolutional Neural Networks Implementation
- Integration with Diagnostic Equipment
Our collaboration with Japanese researchers has been instrumental in pushing the boundaries of what's possible in early cancer detection. The precision of our CNN model is a game-changer for the medical community.
Lead Research Scientist
- Clinical validation phase
- In progress
- Diagnostic workflow speed
- Improved
- Time-to-review for clinicians
- Reduced
- Model performance optimization
- Ongoing