In their study, He et al. 1 evaluated a computed tomography angiography (CTA) based method of plaque quantification and rational parameters for stent oversizing in the femoropopliteal artery and their influence on clinical outcomes following femoropopliteal stenting. They showed that high calcified plaque burden and excessive stent oversizing were associated with unfavourable outcomes. These promising results could directly influence clinical practice, since pre-operative CTA, now considered one of the reference imaging modalities in peripheral arterial disease, is available for most patients scheduled for femoropopliteal procedures, and the exquisite amount of detail it gives is far from being used to its fullest potential. 2,3 However, all the CT quantification techniques used in these types of studies require complex and time-consuming post-processing, incompatible with routine clinical workflow. 1,4,5 One could imagine that this major practical limitation might soon be waived with the recent advances in the field of artificial intelligence. Deep learning methods applied to a big CTA dataset 3 could have the power to easily and comprehensively synthesize complete functional and morphological assessment from the anatomy (i.e. the CTA), correlate it with various outcomes following endovascular treatment and guide our choices in the way the procedure is done. Another important point is that He et al. only focused on calcified plaques. 1 CTA analysis allows the evaluation of the whole atherosclerotic plaque based on the Hounsfield unit attenuation profiles of the different plaque components, as already established in the carotid, coronary, and peripheral arteries. 5 It would therefore have been interesting to evaluate the whole plaque, analysing calcified but also necrotic or fibrous areas, as done by Patel et al. 5 They have demonstrated that only the amount of calcification was related to the outcomes, but their study was lacking power and one could still think that different plaque characteristics and composition, beyond simple calcification quantification, might also affect prognosis and outcomes.