Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

ABSTRACT:

Background

There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.

Methods

We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (

SUBMITTER: D'Anniballe VM 

PROVIDER: S-EPMC9011942 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature