Scholarlink

<p>The Z-score is a conceptually simple and widely adopted standard for assessing aortic dilatation from echocardiographic measurements. It is routinely used to monitor patient progression and schedule follow-up checks. However, several criticisms have been raised due to the intrinsic limitations of the typically homoscedastic and linear predictive models. In this paper, we reinterpret the Z-score as a quantitative measure of the <i>aleatoric uncertainty</i> affecting aortic diameters, after indexing by a limited number of predictive variables. This view reveals an additional, previously overlooked limitation: the presence of <i>epistemic uncertainty</i>, arising from limited or biased reference datasets. When epistemic uncertainty is high, the Z-score becomes unreliable, yet current tools fail to indicate this. We therefore adopt a Bayesian reformulation based on heteroscedastic Gaussian process regression, where diameters and their aleatoric uncertainties are modeled as random variables. In this framework, the Z-score itself is random, and clinicians receive both an expected value and a <i>high density interval</i> quantifying epistemic uncertainty. Trained on a merged dataset of 1,947 healthy subjects, our Bayesian Z-score detects more dilatations in at-risk patients, identifies uncertain cases, and offers a more reliable basis for clinical decision-making.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards a more reliable assessment of aortic diameters using a Bayesian Z-score

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

查看全文
  • Luca Bindini,
  • Laurence Campens,
  • Jesse Davis,
  • Laura Muiño-Mosquera,
  • Simon D’hulst,
  • Julie De Backer,
  • Stefano Nistri,
  • Paolo Frasconi

摘要

The Z-score is a conceptually simple and widely adopted standard for assessing aortic dilatation from echocardiographic measurements. It is routinely used to monitor patient progression and schedule follow-up checks. However, several criticisms have been raised due to the intrinsic limitations of the typically homoscedastic and linear predictive models. In this paper, we reinterpret the Z-score as a quantitative measure of the aleatoric uncertainty affecting aortic diameters, after indexing by a limited number of predictive variables. This view reveals an additional, previously overlooked limitation: the presence of epistemic uncertainty, arising from limited or biased reference datasets. When epistemic uncertainty is high, the Z-score becomes unreliable, yet current tools fail to indicate this. We therefore adopt a Bayesian reformulation based on heteroscedastic Gaussian process regression, where diameters and their aleatoric uncertainties are modeled as random variables. In this framework, the Z-score itself is random, and clinicians receive both an expected value and a high density interval quantifying epistemic uncertainty. Trained on a merged dataset of 1,947 healthy subjects, our Bayesian Z-score detects more dilatations in at-risk patients, identifies uncertain cases, and offers a more reliable basis for clinical decision-making.