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Related Concept Videos

Uncertainty: Overview00:59

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.
Propagation of Uncertainty from Random Error00:59

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...
Propagation of Uncertainty from Systematic Error01:10

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 particular...
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
Uncertainty: Confidence Intervals00:54

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 't,' or...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

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  1. Home
  3. Autonomous Uncertainty Quantification For Computational Point-of-care Sensors.
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  3. Autonomous Uncertainty Quantification For Computational Point-of-care Sensors.

Related Experiment Video

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Autonomous Uncertainty Quantification for Computational Point-of-Care Sensors.

Artem Goncharov1, Rajesh Ghosh2, Hyou-Arm Joung1

  • 1Electrical & Computer Engineering Department, University of California, Los Angeles, California 90095, United States.

ACS Nano
|June 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an uncertainty quantification method for computational point-of-care (POC) diagnostics. It enhances Lyme disease detection accuracy by identifying and excluding unreliable neural network predictions, improving diagnostic sensitivity.

Keywords:
Lyme diseaseMonte Carlo dropoutcomputational point-of-care sensorsneural networksuncertainty quantificationvertical flow assays

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Area of Science:

  • Biomedical Engineering
  • Computational Diagnostics
  • Infectious Disease Detection

Background:

  • Computational point-of-care (POC) sensors offer rapid diagnostics but neural networks risk erroneous predictions.
  • Accurate diagnostics are crucial in resource-limited settings for timely medical intervention.
  • Lyme disease, a prevalent tick-borne illness, requires accessible and reliable diagnostic tools.

Purpose of the Study:

  • To develop an autonomous uncertainty quantification technique for computational POC diagnostics.
  • To enhance the reliability and accuracy of neural network-based diagnostic models.
  • To improve the sensitivity and robustness of POC diagnostic systems for diseases like Lyme disease.

Main Methods:

  • Developed and implemented a Monte Carlo dropout (MCDO)-based uncertainty quantification approach.
  • Integrated MCDO into a paper-based, computational vertical flow assay (xVFA) platform for Lyme disease diagnosis.
  • Utilized a neural network inference algorithm within the xVFA system for autonomous error exclusion.

    • Main Results:

    • The uncertainty quantification method successfully identified and excluded erroneous predictions with high uncertainty.
    • Diagnostic sensitivity of the xVFA platform for Lyme disease increased from 88.2% to 95.7% in blinded testing.
    • The approach improved the reliability of neural network-driven computational POC sensing systems with minimal overhead.

    Conclusions:

    • Autonomous uncertainty quantification significantly enhances the accuracy and reliability of computational POC diagnostics.
    • The MCDO-based technique offers a robust solution for improving neural network performance in diagnostic applications.
    • This advancement holds promise for more dependable diagnostics in emergency and resource-limited settings.