Post-infarction scar remodelling may start and evolve at a different pace across patients, often requiring years until the affected tissue becomes arrhythmogenic. Current risk stratification strategies lack sensitivity and specificity, and therefore more knowledge is required (e.g. new biomarkers and insights about the appropriate time point to evaluate them). We introduce a model of scar maturation and a novel estimator of scar remodelling based on fully automated analysis of cardiac computed tomography (CT). We retrospectively included patients with history of myocardial infarction and available cardiac CT at the scarring stage (i.e. >6 months post-infarction). A fully automated pipeline was introduced including LV wall segmentation, reformatting to short-axis view, and LV wall thickness (WT) computation. 4 biomarkers representing moderate to severe LV wall thinning were considered (i.e. WT < {5, 4, 3, 2} mm). A statistical approach based on Bayesian Gaussian Process regression was used to model the biomarkers’ evolution. The Scar Maturation Score, a subject-specific parameter encoding the maturation stage relatively to the group-wise model was introduced, and its relationship with sustained ventricular arrhythmia (VA) was analysed. A total of 428 patients were included (age: 73±8 years, 83% males, LVEF: 46±11%), comprising 58 with 2 CT studies available (median scan delay: 2.1 years). The model estimated a progressive LV wall thinning over the years following the infarction. Patients without VA were typically assigned to low Scar Maturation Scores (i.e. early maturation stage), while VA patients were frequently associated with higher Scores. Scar Maturation Score, LVEF, area of wall thinning, and scar age were all significantly associated with VA (p < 0.0001), the Scar Maturation Score showing the higher correlation ratio. Fully-automated LV thickness measurements from CT allow for a plausible modelling of scar maturation that, together with the patient-specific Scar Maturation Score, may be useful to improve patient treatment (e.g. determine optimal time point for patient screening) and risk stratification.