Survival Analysis
Pierwszy klinicznie zwalidowany test prognostyczny/predykcyjny, zbudowany od podstaw z wykorzystaniem sztucznej inteligencji, dla pacjentek z inwazyjnym rakiem piersi. Ataraxis Breast wykorzystuje multimodalne dane kliniczne, w tym slajdy ze standardowych preparatów histopatologicznych, aby wygenerować personalizowany profil ryzyka pacjentów, i pomóc podjąć decyzję na temat leczenia we wszystkich podtypach raka piersi.
Why survival analysis matters
Most machine learning models treat prediction as a simple classification, such as high risk or low risk. Survival analysis goes further, estimating the likelihood of recurrence across specific time horizons such as within 5 years, 10 years, or beyond. A 20% chance of recurrence within 2 years represents a very different clinical situation than a 20% chance over 10 years, and the difference matters for treatment decisions. By accounting for incomplete data, survival analysis provides a richer, more clinically meaningful view of prognosis that better guides treatment intensity, surveillance, and follow-up.
Our models train on diverse real-world datasets spanning multiple institutions and cancer types, capturing how disease risk shifts over time and surfacing temporal patterns that conventional approaches miss. Pathology and clinical signals are trained together so the system learns jointly from both modalities, using time-to-event loss functions including Cox and discrete-time survival models.
By combining the interpretability of survival analysis with the representational power of foundation models, we transform prognosis from a fixed snapshot into a dynamic, patient-specific prediction that adapts to individual biology and clinical context.