| Title | Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm. |
| Publication Type | Journal Article |
| Year of Publication | 2018 |
| Authors | Nguyen TD, Zhang S, Gupta A, Zhao Y, Gauthier SA, Wang Y |
| Journal | AJNR Am J Neuroradiol |
| Volume | 39 |
| Issue | 5 |
| Pagination | 830-833 |
| Date Published | 2018 05 |
| ISSN | 1936-959X |
| Keywords | Adult, 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). |
| DOI | 10.3174/ajnr.A5594 |
| Alternate Journal | AJNR Am J Neuroradiol |
| PubMed ID | 29519791 |
| PubMed Central ID | PMC5955764 |
| Grant List | R01 NS090464 / NS / NINDS NIH HHS / United States |