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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.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

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...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

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...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

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: Nov 17, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis.

Afshar Shamsi, Hamzeh Asgharnezhad, Shirin Shamsi Jokandan

IEEE Transactions on Neural Networks and Learning Systems

|February 11, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning framework for COVID-19 detection using medical images, offering an alternative to PCR tests. The model identifies COVID-19 cases with high accuracy and provides uncertainty estimates for reliable diagnosis.

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Infectious Disease Diagnostics

Background:

Reliable COVID-19 detection is crucial for outbreak control.
Limitations exist with Polymerase Chain Reaction (PCR) tests, including availability and accuracy concerns.

Purpose of the Study:

To develop a deep learning framework for COVID-19 detection using medical images.
To incorporate uncertainty quantification for improved diagnostic reliability.

Main Methods:

Utilized four Convolutional Neural Networks (CNNs): VGG16, ResNet50, DenseNet121, and InceptionResNetV2 for feature extraction from X-ray and CT images.
Applied machine learning models, including linear support vector machines and neural networks, for classification.
Calculated epistemic uncertainty to identify model confidence in predictions.

Main Results:

Linear support vector machine and neural network models demonstrated superior performance in accuracy, sensitivity, specificity, and AUC.
Higher predictive uncertainty was observed for Computed Tomography (CT) images compared to X-ray images.
The framework effectively identifies COVID-19 cases from medical imaging data.

Conclusions:

The proposed deep uncertainty-aware transfer learning framework offers a viable alternative for COVID-19 detection.
Uncertainty estimation is valuable for assessing model confidence, particularly with CT imaging.
Machine learning models show promise in enhancing the accuracy and reliability of COVID-19 diagnostics.