In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to...
Convolutional neural networks (CNNs) are often used in the area of image recognition. We constructed the three dimensional (3D)-CNN model to predict pulmonary...
The authors of this study aimed to evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise...
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...
We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there...
In this retrospective study, the authors aimed to develop a fully automated artificial intelligence (AI) system to quantitatively assess the severity and...
This study used a sample of 131 participants who underwent low-dose computed tomography (LDCT) and standard-dose computed tomography (SDCT) to determine the...
In this study, the authors proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data, which...
Endometrial cancer (EC) has the highest rate of malignancy in women in the entire world, including China, which has the largest population. Accurately staging...
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.
