一个无模型的框架,用于评估新设备的可靠性,使用多个不完美的参考标准
Ying Cui1, Qi Yu1, Amita Manatunga1
1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30329, United States.
Biometrics
|March 17, 2025
概括
在没有黄金标准的情况下,评估新的计算机辅助诊断 (CAD) 设备是具有挑战性的. 本研究引入了一个统计框架,用于评估CAD设备的可靠性,使用多个不完美的参考标准,考虑准确度的变化.
科学领域:
- 医疗成像医学成像
- 生物统计学 生物统计学
- 诊断的准确性 诊断的准确性
背景情况:
- 评估计算机辅助诊断 (CAD) 设备通常依赖于黄金标准测试.
- 临床研究中通常无法使用黄金标准,因此需要使用多个不完美的参考标准.
- 跨参考标准的诊断准确性的异质性可能会导致设备评估偏差.
研究的目的:
- 开发一个统计框架,用多个不完美的参考标准来评估CAD设备.
- 解决参考标准之间异质诊断准确性的挑战.
- 为设备可靠性评估提供直观,易于使用的方法.
主要方法:
- 一个统计框架,评估CAD设备与多个不完美的参考标准的加权和值之间的一致性.
- 一种无模型,无监督的诱导程序,根据参考标准的相对可靠性来确定参考标准的权重.
- 递归权重分配有利于更一致和多数意见的参考标准.
主要成果:
- 拟议的框架有效地通过考虑不同参考标准的准确性来评估CAD设备.
- 权重分配给参考标准,不需要建模假设或外部数据.
- 该方法在对多个医生评估对脏阻塞的CAD设备的评估中证明了其实用性.
结论:
- 开发的统计框架为在缺少黄金标准的情况下评估CAD设备提供了可靠的方法.
- 无监督权重程序有效地处理参考标准之间的精度异质性.
- 这种方法提供了一种可靠的工具,用于评估诊断设备在现实世界的临床环境中的性能.
相关概念视频
03:37Uncertainty in Measurement: Accuracy and Precision
73.1K
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.
73.1K
01:15Systematic Error: Methodological and Sampling Errors
1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K
01:19Testing a Claim about Standard Deviation
2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
01:11Statistical Analysis: Overview
5.5K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
5.5K
01:20Random and Systematic Errors
10.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.7K
01:15Data Validation
132
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
132