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Uncertainty: Overview

Uncertainty: Overview

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.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

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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Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

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A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications.

Vinzenz Gregor Eck¹, Wouter Paulus Donders², Jacob Sturdy¹

¹Division of Biomechanics, Department of Structural Engineering, NTNU, Trondheim, Norway.

International Journal for Numerical Methods in Biomedical Engineering

|October 18, 2015

Summary

This summary is machine-generated.

Quantifying uncertainty in cardiovascular models is crucial for patient-specific medicine. This study presents practical methods for uncertainty quantification (UQ) and sensitivity analysis (SA) to improve prediction reliability.

Keywords:

Monte Carlo arterial compliance cardiovascular modeling fractional flow reserve polynomial chaos sensitivity analysis uncertainty quantification

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Area of Science:

Computational biology
Biomedical engineering
Mathematical modeling

Background:

The shift towards patient-specific medicine necessitates reliable cardiovascular models.
Accurate prediction of model outputs requires understanding input uncertainties.
Uncertainty quantification (UQ) and sensitivity analysis (SA) are essential for clinical decision-making.

Purpose of the Study:

To explain global UQ and variance-based SA methods.
To propose a practical six-step guide for UQ and SA.
To demonstrate UQ and SA on cardiovascular models for FFR and CT estimation.

Main Methods:

Global uncertainty quantification (UQ).
Global, variance-based sensitivity analysis (SA).
Monte Carlo (MC) and polynomial chaos (PC) methods applied to cardiovascular models.

Main Results:

MC and PC methods yield identical results for identifying significant model inputs.
Polynomial chaos (PC) is more cost-efficient than Monte Carlo (MC).
Targeted uncertainty reduction in key inputs effectively minimizes prediction uncertainty.

Conclusions:

This work provides a practical guide for UQ and SA in cardiovascular modeling.
The proposed methods enhance the clinical applicability of mathematical models.
UQ and SA are vital for advancing patient-specific cardiovascular care.