An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs.

ABSTRACT:

Objectives

Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are frequently used as a first-line test but the availability of immediate expert radiological review is variable. The aim was to investigate the feasibility of using a deep learning model for automated identification of small bowel obstruction.

Methods

A total of 990 plain abdominal radiographs were collected, 445 with normal findings and 445 demonstrating small bowel obstruction. The images were labelled using the radiology reports, subsequent CT scans, surgical operation notes and enhanced radiological review. The data were used to develop a predictive model comprising an ensemble of five convolutional neural networks trained us

SUBMITTER: Kim DH 

PROVIDER: S-EPMC8173678 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature