mlsanityData to 14 Oct 2026

When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins

arXiv 2608.01602 ↗First posted 3 Aug 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fixed reference convention. Across four shared-backbone echocardiographic EF front-ends, phase conditioning appears to remove CAMUS baseline bias. Matched-reference analysis rejects this gain: singleplane ground-truth EF error is statistically indistinguishable across models, while single-plane ground-truth EF exceeds CAMUS biplane clinical EF by +6.30 points, explaining nearly all baseline bias. A prespecified EchoNet-Dynamic replication, with released data and our extractor aligned to the apical four-chamber plane, removes baseline overestimation and reverses the CAMUS ranking. We also quantify haemodynamic effects, conformal residual-width budgets, and EF-stratum changes, yielding a Convention-Aware EF Audit protocol that separates genuine observation operator calibration from measurement artefacts. GitHub: EjectionFraction-Bias-in-Cardiac-Digital-Twin.git

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 3 Aug 2026.

Builds on

3 of 21 references.

References · 3

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  3. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 244

Built on by

Not cited yet.