A working research demonstrator
Better cohorts.
Clearer evidence.
Create an artificial cohort, reconstruct hidden information and see how closely the results match the reference. Every activity explains what works today and what still needs validation.
Artificial clinical records and licensed public CT research data. No patient upload. No clinical decisions.
What would you like to explore?
Start with an activity. Open deeper evidence when you need it.
Generate a cohort
Set a representation target or reproduce the reference mix. Inspect fidelity, quotas and safeguards.
Open this activityReconstruct missing information
Hide known artificial values, inspect the predictions, then compare performance across variables.
Open this activityLook inside CT reconstruction
Explore original, masked and reconstructed images. Compare methods and neural network evidence.
Open this activityThree results to start with
Evidence visible at a glance.
These figures come from stored artificial or public-data engineering studies. They are evidence of implemented workflows, not official KPI attainment or clinical validation.
Operational capabilities demonstrated today
A neural network that learned to reconstruct missing CT slices.
The published study compares a residual U-Net with interpolation as the training pool grows. This is measured engineering evidence, not whole-acquisition synthesis.
Explore the neural network study →median masked-slice error reduction versus linear interpolation
Where does the cohort come from?
The clinical reference is generated artificially for this demonstration. It is not a hospital cohort, a representative cancer population or the official evaluation dataset.
Working demonstrations. Further validation ahead.
A ready software harness is different from an achieved scientific target. Authorised data, approved definitions and locked evaluation are prerequisites for official results.
Understand the remaining prerequisites →