Work

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

Year: 2018

Type: article

Abstract: SUMMARY: 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).

Source: American Journal of Neuroradiology

DOI: 10.3174/ajnr.a5594

Authors: T.D. Nguyen, S. Zhang, A. Gupta, Y. Zhao, S.A. Gauthier, Y. Wang

Institutions: Cornell University, Weill Cornell Medicine

Language: English

Topic: Multiple Sclerosis Research Studies

Subfield: Pathology and Forensic Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Multiple Sclerosis Society, National Institutes of Health, National Institute of Neurological Disorders and Stroke