ML-Driven Embryo Viability Prediction System
Machine learning on real-time incubation imaging, supporting clinicians on the embryos most likely to yield safer, single-pregnancy outcomes.
Multiple live births from IVF procedures increase health risks for mothers and babies while driving up costs. In this engagement, we built machine learning models that analyze real-time incubation image feeds to identify the embryos most likely to yield optimal outcomes.
The system was developed in close collaboration with clinical leadership to support safer decision-making and reduce the likelihood of high-risk outcomes.
The decision
The decision was to use machine learning as clinical decision support, not as a replacement for clinical judgment. Predictive models would analyze real-time incubation imaging and produce outcome-focused scoring inside a traceable, clinician-aligned workflow developed with medical leadership.
Intelligence layer
- ML models trained on incubation imaging to predict embryo viability.
- Real-time image feed processing to support timely clinical decision-making.
- Clinician-aligned workflows designed with medical leadership input.
- Outcome-focused scoring to reduce multiples and improve safety.
- Traceability to support reviewability and clinical governance.
Insights Earned Across Industries
Every industry has its own language. But many of the hardest software problems repeat: messy workflows, disconnected systems, complex permissions, reporting gaps, and users who need better tools to do their jobs. Stratos works across healthcare, financial services, benefits, logistics, real estate, education, music technology, and other operationally complex sectors. That breadth gives us more than category familiarity. It gives us a working library of proven patterns. Clients benefit not only from what we understand about their industry, but from what we have already solved in others.
Reusable lessons. Faster decisions. Stronger software.