ClarusC64/ffr-physiology-prediction-coherence-baseline-mapping-v0.1

Goal

Define the baseline coherence between AI-derived FFR predictions and real physiological signals.

Signals include:

• —myocardial perfusion
• —wall motion
• —stress test results
• —vital signs

This dataset establishes what physiologically plausible alignment looks like.

Without this baseline implausibility cannot be detected.

Required output

The model must provide:

• —physiologicalcoherencescore
• —interpretation of alignment error
• —baseline_label

Why this matters

AI-FFR can remain stable numerically while becoming physiologically implausible.

This dataset detects the moment prediction and physiology stop telling the same story.

It enables:

• —cardiology validation workflows
• —deployment safety checks
• —regulator review
• —multi-modality consistency testing

Evaluation

The scorer checks:

• —coherence reasoning present
• —numeric interpretation
• —baseline classification

Future versions will include true regression scoring against ground-truth coherence values.