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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Related Experiment Video

Updated: Sep 13, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
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Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved

Karthik Menon1, Andrea Zanoni2, M Owais Khan3

  • 1Woodruff School of Mechanical Engineering and Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Computer Methods and Programs in Biomedicine
|July 30, 2025
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Summary

This study introduces an uncertainty-aware pipeline for personalized coronary flow simulations using CT myocardial perfusion imaging. The new method improves prediction precision and reduces computational costs for coronary artery disease risk stratification.

Keywords:
Bayesian parameter estimationCoronary artery flowsMulti-fidelity uncertainty quantification

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Area of Science:

  • Cardiovascular Physiology
  • Computational Fluid Dynamics
  • Medical Imaging Analysis

Background:

  • Non-invasive coronary hemodynamics simulations enhance risk stratification for coronary artery disease (CAD).
  • Current simulation methods often use empirical flow distribution, neglecting patient-specific factors and data uncertainty.
  • Accurate modeling requires incorporating individual variability, disease states, and clinical data uncertainties.

Purpose of the Study:

  • To develop an end-to-end pipeline for personalized coronary flow simulations.
  • To integrate vessel-specific flows and cardiac function, accounting for clinical data uncertainty.
  • To enhance the precision of predicting clinical and biomechanical outcomes.

Main Methods:

  • Assimilated patient-specific myocardial blood flow from CT perfusion imaging to estimate branch-specific coronary artery flows.
  • Employed adaptive Markov Chain Monte Carlo sampling to estimate model parameters under simulated measurement noise.
  • Utilized multi-fidelity Monte Carlo estimation with non-linear dimensionality reduction for posterior predictive distributions.

Main Results:

  • The framework accurately reproduced cardiac function and branch-specific flows, accounting for measurement uncertainty.
  • Observed significant reductions in confidence intervals compared to single- and multi-fidelity Monte Carlo methods.
  • Achieved reduced computational cost for multi-fidelity Monte Carlo estimators while maintaining specified confidence levels.

Conclusions:

  • The developed pipeline enables personalized, uncertainty-aware predictions of coronary hemodynamics using routine clinical data.
  • Leverages advanced CT myocardial perfusion imaging techniques for improved accuracy.
  • Offers substantial improvements in predictive precision and computational efficiency for clinical applications.