This study aims to develop and validate a deep learning-based automatic chest radiograph (CXR) cardiovascular border (CB) analysis algorithm (CB_auto) in...
The aim of this study was to qualitatively explore the perception of radiographers in relation to the integration and acceptance of artificial intelligence...
The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving...
Our study included 519 screening chest radiographs (CXRs) from 294 patients enrolled in the National Lung Screening Trial (NLST) who either had proven to have...
The purpose of this retrospective study was to evaluate whether initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in...
The purpose of this study was to classify the most common types of plain radiography through the use of a neural network and, subsequently, to validate the...
Chest radiographs (CRs) have long been used as one of the screening tests for pulmonary tuberculosis (TB). However, the interpretation of a large number of...
The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using...
In an age of uncertainty with the arrival of artificial intelligence (AI) tools and technologies in the healthcare field, many in the industry question how...
Welcome to the blog on Artificial Intelligence of the European Society of Radiology
This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.
