artificial intelligence

Artificial CT images can enhance variation of case images in diagnostic radiology skills training

**Objectives** We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided. **Methods** Artificial images were generated with a generative model trained …

European Radiology AI Blog: Airway Training Set Creation

The European Radiology AI Blog recently published a news piece about our publication about creating detailed ground truth segmentations of airways. You can find the full blog post here.

Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium

Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test …

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the …

Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage

To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection in CT scans. The public …

Manual Correction of CT-Derived Airway Segmentations for Artificial Intelligence Applications: A Technical Note

Artificial Intelligence (AI) tools provide rapid analysis of complex datasets, at the cost of flexibility in the data that is fed to them. To have the best performance, AI tools require training on data similar to the data that will be encountered …

Automated Pipeline for XNAT Data Bulk Export

Lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease, the so-called Big-3 (B3), are responsible for high rates of morbidity and mortality. B3CARE is a research collaboration project, with a final ambition to establish an …

B3CARE XNAT-Based Research Infrastructure for Imaging Biomarker Evaluation

The B3CARE project aims to validate and evaluate imaging biomarkers for the Big-3 diseases (lung cancer, COPD, and cardiovascular disease). To this end a large-scale, high- quality imaging data biobank is established containing data from different …

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. …