Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

ABSTRACT:

Objectives

To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.

Design

A retrospective case-control study was undertaken.

Setting

Community radiology clinics and hospitals in Australia and the USA.

Participants

A test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoraci

SUBMITTER: Seah J 

PROVIDER: S-EPMC8655590 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature