Fast and robust unsupervised identification of MS lesion change using the statistical detection of changes algorithm
© 2018 American Society of Neuroradiology. All rights reserved. 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 lesionprediction 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).
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