Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans.

ABSTRACT: Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement dete

SUBMITTER: Ye Z 

PROVIDER: S-EPMC9152686 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature