Multi-Reader-Multi-Split Annotation of Emphysema in Computed Tomography.

ABSTRACT: Emphysema is visible on computed tomography (CT) as low-density lesions representing the destruction of the pulmonary alveoli. To train a machine learning model on the emphysema extent in CT images, labeled image data is needed. The provision of these labels requires trained readers, who are a limited resource. The purpose of the study was to test the reading time, inter-observer reliability and validity of the multi-reader-multi-split method for acquiring CT image labels from radiologists. The approximately 500 slices of each stack of lung CT images were split into 1-cm chunks, with 17 thin axial slices per chunk. The chunks were randomly distributed to 26 readers, radiologists and radiology residents. Each chunk was given a quick score concerning emphysema type and severity in the left a

SUBMITTER: Liden M 

PROVIDER: S-EPMC7572947 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature