In this study, the authors aimed to investigate the effects of plaque-related factors, if any, on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). This was undertaken by analyzing 1,224 vessels in 306 patients. The authors were able to determine that AI-CADS has the ability to distinguish ≥50% coronary stenosis, but found that an additional manual interpretation based on AI-CADS is necessary. Key points AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence. Additional manual interpretation on the basis of AI-CADS is necessary. The plaque length and CACs will affect the diagnostic performance of AI-CADS. Article: Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography Authors: Jie Xu, Linli Chen, Xiaojia Wu, Chuanming Li, Guangyong Ai, Yuexi Liu, Bitong Tian, Dajing Guo & Zheng Fang

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

