Related Experiment Video
Updated: Feb 13, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm
T D Nguyen1, S Zhang2, A Gupta2,3
1From the Department of Radiology (T.D.N., S.Z., A.G., Y.W.) tdn2001@med.cornell.edu.
Abstract:
We developed a robust automated algorithm called statistical detection of changes for detecting morphologic changes of multiple sclerosis lesions between 2 T2-weighted FLAIR brain images. Results from 30 patients showed that statistical detection of changes achieved significantly higher sensitivity and specificity (0.964, 95% CI, 0.823-0.994; 0.691, 95% CI, 0.612-0.761) than with the lesion-prediction algorithm (0.614, 95% CI, 0.410-0.784; 0.281, 95% CI, 0.228-0.314), while resulting in a 49% reduction in human review time (P = .007).
Related Concept Videos
Statistical Significance
Probability in Statistics
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Introduction to Statistics
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
Trial and Error and Algorithm
Statistical Analysis: Overview
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...
Identifying Statistically Significant Differences: The F-Test

