In this study, using a semi-automatic software as a reference, the authors aimed to evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software. This observational study included 315 non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. The authors determined that there was a strong correlation between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Key points Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming. Article: Evaluation of an AI-based, automatic coronary artery calcium scoring software Authors: Mårten Sandstedt, Lilian Henriksson, Magnus Janzon, Gusten Nyberg, Jan Engvall, Jakob De Geer, Joakim Alfredsson & Anders Persson

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a

