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Synthetic demonstration data — not for clinical use. Screens and figures on this site are illustrative. VerityRT is not FDA-cleared and makes no claim of clinical validation.

Module

A contour that looks plausible and is wrong is the expensive failure

Contour review is hard because a bad contour rarely looks bad. It looks like a contour. This module makes the uncertainty spatial, compares against what was approved before, and quantifies what the difference would cost in dose.

A single confidence number hides the thing you need to see

Telling a clinician that a structure is 91% confident is close to useless: it does not say where. A structure can agree well overall and be badly wrong at the one boundary that sits inside the high-dose region. The useful output is a map of where the draft is least certain, a comparison against the previously approved version, and a statement of what the difference changes downstream.


What the module does

Names structures correctly
Resolves draft names against your institutional schema and standard nomenclature, so every downstream rule can parse them without a mapping step.
Shows uncertainty spatially
A heat map over the image, not a single number. Where the draft is least certain is where a reviewer should look first.
Runs geometric checks
Discontinuity, islands, overlap, containment, abrupt termination, laterality, volume, surface irregularity and body-boundary violation.
Compares against the record
Dice, surface Dice, 95th-percentile Hausdorff distance, mean surface distance and volume difference against the prior approved structure and against reference cohorts.
Quantifies dose impact
What changes in the reported metrics if the reviewer accepts the draft rather than the prior version.
Captures every edit
Each correction is recorded with the region, the reason category, a free-text rationale, the reviewer's role, the time spent, whether it was clinically material and what it changed downstream.

What it does not do

Boundaries of this module

  • Approve contours. Final contour approval is a human gate.
  • Define targets autonomously. Target definition is a higher-risk function than organ-at-risk drafting and is treated separately.
  • Retrain on your corrections. Corrections become candidate training data behind adjudication and an offline release.
  • Run outside its validated distribution. A scanner or acquisition outside the validated set is a stop condition, not a degraded result.

See it working

On the synthetic thorax case, one structure is discontinuous across four slices inside the high-dose region. The agent that drew it reports the gap as a correct anatomical boundary at 0.74 confidence. The independent checker disagrees and blocks. Both positions are shown side by side, and a person decides.

Status

Demonstrated on synthetic data with fixture structures. No segmentation model runs in this prototype. Automatic contouring is a regulated function that would require its own intended use, clinical evidence and quality system before any clinical deployment.