In the fight against cancer, technology plays a pivotal role in accelerating diagnostics and deepening our understanding of neoplastic diseases. This case study outlines how Seargin partnered with a world-leading biotech company to develop an advanced analytical system designed to support clinical cancer research. Built on a cutting-edge tech stack, the solution leverages AI algorithms to analyze tissue data, streamline medical research, and improve cancer detection outcomes.
Key Challenges
Building Intelligence into cancer diagnostics:
Select and process tissue scan data
based on medical metadata filters.
Launch AI algorithms
to identify suspected neoplastic changes.
Aggregate and present analytical results
in a clear, actionable format.
Support continuous algorithm training and improvement
through real-world data.
Key components delivered
From Metadata to Machine Learning
The system architecture was based on “Studies”—data containers holding multiple slides (i.e., digital tissue scans). Each slide came enriched with metadata such as: tumor type, biomarkers, protocol numbers, patient identifiers. This foundation enabled the application to perform:
- Multi-layered data filtering
- Researchers could apply detailed filters to select subsets of slides based on medical or research criteria. This ensured precision in the analysis phase.
- AI-powered analysis
- Once a subset was selected, the system triggered an AI algorithm capable of:
- • Detecting areas with suspected neoplastic changes.
- • Flagging high-risk regions for further medical evaluation.
- • Improving detection accuracy with each iteration.
- Aggregated result visualization
- Users received a summary dashboard detailing results such as:
- | “Out of 150 slides, cancer detected in 100; 50 slides clear.”
- This allowed researchers to make quick, data-backed decisions and focus on high-priority samples.
- Algorithm training & continuous learning
- The platform was also designed to improve the AI model over time by incorporating new slide data, feedback loops, and user validations—turning real-world input into better diagnostic capability.
Business results
Revolutionizing clinical research with AI:
Accelerated slide analysis
Proactive issue resolution powered by event management and predictive analytics led to fewer service disruptions and faster incident resolution.
Improved accuracy and consistency
The AI’s learning capabilities ensured that with each new dataset, its predictions became more precise and reliable.
Informed decision-making
Visual reports and aggregated metrics gave clinical teams and data scientists clear, actionable insights.
Scalable architecture
With Kubernetes, Docker, and AWS at its core, the system is built for future scalability and cross-departmental deployment.
