Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm.

ABSTRACT:

Objective

We aimed to develop a deep learning artificial intelligence (AI) algorithm to detect impacted animal bones on lateral neck radiographs and to assess its effectiveness for improving the interpretation of lateral neck radiographs.

Methods

Lateral neck radiographs were retrospectively collected for patients with animal bone impaction between January 2010 and March 2020. Radiographs were then separated into training, validation, and testing sets. A total of 1733 lateral neck radiographs were used to develop the deep learning algorithm. The testing set was assessed for the stand-alone deep learning AI algorithm and for human readers (radiologists, radiology residents, emergency physicians, ENT physicians) with and without the aid of the AI algorithm. Another radiograph

SUBMITTER: Chen YS 

PROVIDER: S-EPMC10020388 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature