Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm.
Thanh D NguyenShun ZhangAjay GuptaYize ZhaoSusan A GauthierYi WangPublished in: AJNR. American journal of neuroradiology (2018)
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).
Keyphrases
- deep learning
- machine learning
- multiple sclerosis
- loop mediated isothermal amplification
- end stage renal disease
- label free
- real time pcr
- endothelial cells
- newly diagnosed
- white matter
- chronic kidney disease
- magnetic resonance
- convolutional neural network
- prognostic factors
- ms ms
- peritoneal dialysis
- high throughput
- magnetic resonance imaging
- neural network
- blood brain barrier
- patient reported outcomes
- sensitive detection
- functional connectivity
- bioinformatics analysis
- contrast enhanced