Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance.

ABSTRACT:

Purpose

To compare sensitivity in the detection of lung nodules between the deep learning (DL) model and radiologists using various patient population and scanning parameters and to assess whether the radiologists' detection performance could be enhanced when using the DL model for assistance.

Materials and methods

A total of 12 754 thin-section chest CT scans from January 2012 to June 2017 were retrospectively collected for DL model training, validation, and testing. Pulmonary nodules from these scans were categorized into four types: solid, subsolid, calcified, and pleural. The testing dataset was divided into three cohorts based on radiation dose, patient age, and CT manufacturer. Detection performance of the DL model was analyzed by using a free-response receiver operati

SUBMITTER: Liu K 

PROVIDER: S-EPMC8017422 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature