Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.

ABSTRACT:

Objective

To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.

Methods

168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).

Results

All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2

SUBMITTER: Molinski NS 

PROVIDER: S-EPMC10899383 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature