Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction: The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods: A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results: ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice

Balloon dilatation and stenting for aortic coarctation: a systematic review and meta-analysis

Background—There is no systematic assessment of available evidence on effectiveness and comparative effectiveness of balloon dilatation and stenting for aortic coarctation. Methods and Results—We systematically searched 4 online databases to identify and select relevant studies of balloon dilatation and stenting for aortic coarctation based on a priori criteria (PROSPERO 2014:CRD42014014418). We quantitatively synthesized results for each intervention from single-arm studies and obtained pooled estimates for relative effectiveness from pairwise and network meta-analysis of comparative studies. Our primary analysis included 15 stenting (423 participants) and 12 balloon dilatation studies (361 participants), including patients ≥10 years of age. Post-treatment blood pressure gradient reduction to ≤20 and ≤10 mm Hg was achieved in 89.5% (95% confidence interval, 83.7–95.3) and 66.5% (44.1–88.9%) of patients undergoing balloon dilatation, and in 99.5% (97.5–100.0%) and 93.8% (88.5–99.1%) of patients undergoing stenting, respectively. Odds of achieving ≤20 mm Hg were lower with balloon dilatation as compared with stenting (odds ratio, 0.105 [0.010–0.886]). Thirty-day survival rates were comparable. Numerically more patients undergoing balloon dilatation experienced severe complications during admission (6.4% [2.6–10.2%]) compared with stenting (2.6% [0.5–4.7%]). This was supported by meta-analysis of head-to-head studies (odds ratio, 9.617 [2.654–34.845]) and network meta-analysis (odds ratio, 16.23, 95% credible interval: 4.27–62.77) in a secondary analysis in patients ≥1 month of age, including 57 stenting (3397 participants) and 62 balloon dilatation studies (4331 participants). Conclusions—Despite the limitations of the evidence base consisting predominantly of single-arm studies, our review indicates that stenting achieves superior immediate relief of a relevant pressure gradient compared with balloon dilatation