"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison.

Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.

Methods: A trained convolutional neural network (CNN) was used for cardiac segmentation in F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation.