Evaluating AI Model Robustness in Cardiovascular MRI: A Deep Dive into 'Nick'

Nick delivers automated CMR segmentation that achieves near‑expert ventricular volumes and left ventricular mass (LVM), offering immediate operational gains for imaging services. In a multi‑center test set of 359 cases spanning healthy volunteers and diverse cardiac pathologies, the model agreed closely with manual gold‑standard segmentations (ventricular volume correlations R²≥0.93; LVM R²=0.86), supporting measurement‑level fidelity for routine parameters and potential time savings in report generation.

The evaluation was a multi‑centdf, retrospective comparison against expert manual segmentations (359 cases acquired at 1.5T and 3T; 104 healthy subjects and 255 patients). Primary endpoints were left and right ventricular volumes and LVM; reported correlations included LVEDV R²=0.95, LVESV R²=0.97 and LVM R²=0.86, with Bland–Altman analyses showing clinically acceptable biases. These quantitative endpoints map directly to clinical needs such as serial follow‑up measurements and pre‑surgical planning where reproducible volumes and mass estimates are required.

Performance remained robust across phenotypes and variable image quality. Errors were infrequent and concentrated in anatomically complex basal and apical slices — on average fewer than two short‑axis slices per case required manual correction — and phase selection and parametric outputs were consistent. In subgroup analyses, Nick sustained agreement in hypertrophic and dilated cardiomyopathies while modestly underestimating certain volumes in isolated cases; overall strong concordance for LV and RV volumes was maintained across tested subgroups.

Compared with manual segmentation, Nick reduces hands‑on time and improves repeatability by producing consistent contours that need minimal post‑processing edits. Contour‑level tolerance testing indicated low manual correction workload and limits of agreement within predefined clinical tolerance ranges, enabling more reproducible serial measurements and standardized reporting templates. Local prospective validation is still advisable before full deployment to account for site‑specific acquisition protocols and vendor‑dependent image characteristics.

Key criteria for assessing AI robustness and generalizability include high correlations across diverse phenotypes and image qualities, external multi‑center testing with non‑overlapping cases, transparent reporting of failure modes and slice‑level correction rates, and prospective operational evaluation. Nick met these benchmarks in the presented evaluation; procurement and local validation should verify the same data‑driven thresholds prior to clinical integration.

Key Takeaways:

• High agreement: Nick shows strong concordance with manual LV/RV volume and LVM quantification (R²≥0.93 for volumes; LVM R²=0.86). Who’s affected: imaging labs and cardiology services. Next step: prospective local validation and pilot integration.
• Operational impact: reduced manual correction and faster throughput with more consistent contours. Who’s affected: technologists and reporting teams. Next step: update QA workflows and provide targeted training.
• Measurement reliability: improved reproducibility for serial studies and trials. Who’s affected: patients in follow‑up and clinical researchers. Next step: adopt within standardized quantification protocols where validated and prepare for regulatory and operational readiness before full deployment.