Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

ABSTRACT: With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs norma

SUBMITTER: Nael K 

PROVIDER: S-EPMC7994311 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature