当测量惯例伪装成心脏数字孪生中的校准增益
When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins
浏览论文内容
中文总结 AI 辅助
该研究针对心脏数字孪生的EF校准问题，通过匹配参考分析及EchoNet-Dynamic复现，提出惯例感知EF审计协议以区分真实校准与测量伪影。
中文摘要 AI 辅助
心脏数字孪生通过观测算子将临床图像转换为生理测量值，但校准研究常采用固定参考惯例。在四个共享主干的超声心动图射血分数（EF）前端中，相位调节似乎消除了CAMUS基线偏差。匹配参考分析否定了该增益：各模型的单平面真实EF误差无统计学差异，且单平面真实EF比CAMUS双平面临床EF高+6.30个百分点，几乎解释了所有基线偏差。预先指定的EchoNet-Dynamic复现（采用公开数据且提取器与心尖四腔平面对齐）消除了基线高估并反转了CAMUS排名。我们还量化了血流动力学效应、共形残差宽度预算及EF分层变化，提出了惯例感知EF审计协议，用于区分真实观测算子校准与测量伪影。GitHub：this http URL
英文摘要
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
发表机构
- College of Engineering and Computer Science, VinUniversity（文朗大学工程与计算机科学学院）
- VinUniversity（文朗大学）
- Center for Innovations in Health Sciences, VinUniversity（文朗大学健康科学创新中心）
机构由 AI 辅助整理，请以论文原文为准。
