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Since the 1990s, endovascular aortic aneurysm repair (EVAR) has become a common alternative to open surgery for the treatment of abdominal aortic aneurysms (AAAs). To aid the deployment of stent‐grafts, fluoroscopic image guidance can be enhanced using preoperative simulation and intraoperative image fusion techniques. However, the impact of calcification (Ca) presence on the guidance accuracy of such techniques is yet to be considered. In the present work, we introduce a guidance tool that accounts for patient‐specific Ca presence. Numerical simulations of EVAR were developed for 12 elective AAA patients, both with (With‐Ca) and without (No‐Ca) Ca consideration. To assess the accuracy of the simulations, the image results were overlaid on corresponding intraoperative images and the overlay error was measured at selected anatomical landmarks. With this approach we gained insight into the impact of Ca presence on image fusion accuracy. Inclusion of Ca improved mean image fusion accuracy by 8.68 ± 4.59%. In addition, a positive correlation between the relative Ca presence and the image fusion accuracy was found (R =.753, p <.005). Our results suggest that considering Ca presence in patient‐specific EVAR simulations increases the reliability of EVAR image guidance techniques that utilize numerical simulation, especially for patients with severe aortic Ca presence. [ABSTRACT FROM AUTHOR]

• The analytical treatment model predicts the deployed stent length more efficiently. • The virtual treatment model is capable of predicting real-time treatment outcomes. • A combination of these two models is recommended for treatment planning. • The models can potentially be used for patient-specific medical device design. The Flow Diverter is a self-expandable braided stent that has helped improve the effectiveness of cerebral aneurysm treatment during the last decade. The Flow Diverter's efficiency heavily relies on proper decision-making during the pre-operative phase, which is currently based on static measurements that fail to account for vessel or tissue deformation. In the context of providing realistic measurements, a biomechanical computational method is designed to aid physicians in predicting patient-specific treatment outcomes. The method integrates virtual and analytical treatment models, validated against experimental mechanical tests, and two patient treatment outcomes. In the case of both patients, deployed stent length was one of the validated result parameters, which displayed an error inferior to 1.5% for the virtual and analytical models. These results indicated both models' accuracy. However, the analytical model provided more accurate results with a 0.3% error while requiring a lower computational cost for length prediction. This computational method can offer designing and testing platforms for predicting possible intervention-related complications, patient-specific medical device designs, and pre-operative planning to automate interventional procedures. [ABSTRACT FROM AUTHOR]

Background: There is a need for a specific atherosclerotic risk assessment for people living with HIV (PLWH)., Setting: A machine learning classification model was applied to PLWH and control subjects with low-to-intermediate cardiovascular risks to identify associative predictors of diagnosed carotid artery plaques. Associations with plaques were made using strain elastography in normal sections of the common carotid artery and traditional cardiovascular risk factors., Methods: One hundred two PLWH and 84 control subjects were recruited from the prospective Canadian HIV and Aging Cohort Study (57 ± 8 years; 159 men). Plaque presence was based on clinical ultrasound scans of left and right common carotid arteries and internal carotid arteries. A classification task for identifying subjects with plaque was defined using random forest (RF) and logistic regression models. Areas under the receiver operating characteristic curves (AUC-ROCs) were applied to select 5 among 50 combinations of 4 or less features yielding the highest AUC-ROCs., Results: To retrospectively classify individuals with and without plaques, the 5 most discriminant combinations of features had AUC-ROCs between 0.76 and 0.79. AUC-ROCs from RF were statistically significantly higher than those obtained with logistic regressions ( P = 0.0001). The most discriminant features of RF classifications in PLWH were age, smoking status, maximum axial strain and pulse pressure (equal weights), and sex and antiretroviral therapy exposure (equal weights). When considering the whole population, the HIV status was identified as a cofactor associated with carotid artery plaques., Conclusions: Strain elastography adds to traditional cardiovascular risk factors for identifying individuals with carotid artery plaques., Competing Interests: C.C.-L. has research collaboration with Siemens Healthineers; G.S. has research grants from Siemens Healthineers, Vitaa Medical, Starpax Medical, and Cook Medical that are not related to this work; and G.C. has research grants from Siemens Healthineers and Vitaa Medical that are not related to this work. The other authors have no conflicts of interest to disclose., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)