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Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm.

TitleFast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm.
Publication TypeJournal Article
Year of Publication2018
AuthorsNguyen TD, Zhang S, Gupta A, Zhao Y, Gauthier SA, Wang Y
JournalAJNR Am J Neuroradiol
Volume39
Issue5
Pagination830-833
Date Published2018 05
ISSN1936-959X
KeywordsAdult, Algorithms, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis
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 ( = .007).

DOI10.3174/ajnr.A5594
Alternate JournalAJNR Am J Neuroradiol
PubMed ID29519791
PubMed Central IDPMC5955764
Grant ListR01 NS090464 / NS / NINDS NIH HHS / United States

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