Explainable AI to identify radiographic features of pulmonary edema.

ABSTRACT:

Background

Pulmonary edema is a leading cause for requiring hospitalization in patients with congestive heart failure. Assessing the severity of this condition with radiological imaging becomes paramount in determining the optimal course of patient care.

Purpose

This study aimed to develop a deep learning methodology for the identification of radiographic features associated with pulmonary edema.

Materials and methods

This retrospective study used a dataset from the Medical Information Mart for Intensive Care database comprising 1000 chest radiograph images from 741 patients with suspected pulmonary edema. The images were annotated by an experienced radiologist, who labeled radiographic manifestations of cephalization, Kerley lines, pleural effusion, bat wings, and in

SUBMITTER: Danilov VV 

PROVIDER: S-EPMC12429240 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature