Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.

ABSTRACT: Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 mult

SUBMITTER: Narayana PA 

PROVIDER: S-EPMC6980901 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature