Dynamic Modeling of Abdominal Aortic Aneurysms 2: Classifiers and Stress Tests

Building on class-specific Z-SINDy models for AAA remodeling in size-shape space, we convert learned dynamics into individualized, probabilistic classifications. We compare two Bayes classifiers: a static approach operating on coordinates, and a dynamic approach operating on derivatives evaluated under the class-specific ODEs. Posterior-predictive ensembles show that coordinate distributions separate gradually over 2.5-5 years, whereas directional information from derivatives becomes discriminative after just 1 year. In head-to-head comparisons, the dynamic classifier exits the ``uncertain'' band (posterior <0.9) markedly sooner than the static alternative, enabling earlier classification with fewer scans. We then stress-test clinical realism. Injecting controlled spatial noise into both features (up to 25% of scale) and degrading temporal sampling from exactly annual to jittered or sparse/irregular intervals, we re-estimate derivatives and re-compute posteriors. Accuracy improves monotonically with postoperative time but degrades predictably with spatial perturbations; temporal irregularity induces modest losses relative to annual imaging yet preserves the dynamic classifier's early advantage. These experiments quantify how segmentation fidelity and scheduling affect decision quality and provide practical guidance for surveillance design. Taken together, the Z-SINDy + dynamic Bayes pipeline links interpretable physics-informed models to actionable, uncertainty-aware predictions on real, heterogeneous clinical data.