Automated detection of IVC filters on radiographs with deep convolutional neural networks.

ABSTRACT:

Purpose

To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.

Methods

A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.

Results

On the primary test set

SUBMITTER: Mongan J 

PROVIDER: S-EPMC9902407 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature