A geometry-informed neural operator (GINO) predicted left ventricular activation time maps with a 4.79% error on real-world geometries, comparable to a graph neural network (4.07% error).
Can geometric deep learning models accurately predict cardiac activation time maps on left ventricular geometries for CRT planning?
Geometric deep learning models can rapidly and accurately predict left ventricular activation maps, offering a potential in-silico tool for personalized CRT optimization.
Absolute Event Rate: 4.79% vs 4.07%
Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond, partly due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and limitations of current individualized planning strategies. In a step toward an in-silico approach, we develop two geometric deep learning models, based on graph neural network (GNN) and geometry-informed neural operator (GINO), to predict activation time maps on left ventricular (LV) geometries in real time. Trained on a large dataset generated from finite-element simulations spanning a wide range of synthetic LV shapes, pacing site configurations, and tissue conductivities, the GINO model outperforms the GNN on synthetic cases (1.38% vs 2.44% error), while both demonstrate comparable performance on real-world LV geometries (GINO: 4.79% vs GNN: 4.07%). Using the trained models, we develop a workflow to identify an optimal pacing site on the LV from a given activation time map and show that both models can robustly recover ground-truth subject-specific parameters from noisy inputs. In conjunction with an interactive web-based interface ( https://dcsim.egr.msu.edu/ ), this study shows potential and motivates future extension toward a clinical decision-support tool for personalized pre-procedural CRT optimization.
Naghavi et al. (Sat,) conducted a other in Dyssynchronous heart failure. Geometry-informed neural operator (GINO) vs. Graph neural network (GNN) was evaluated on Prediction error on real-world LV geometries. A geometry-informed neural operator (GINO) predicted left ventricular activation time maps with a 4.79% error on real-world geometries, comparable to a graph neural network (4.07% error).