Feature-Based Fusion of Angiography Videos and Blood Parameters for Fractional Flow Reserve (FFR) Prediction

Background/Objectives: Estimating fractional flow reserve (FFR) non-invasively in intermediate coronary artery disease remains challenging because anatomical stenosis severity does not consistently correlate with functional ischemia. While conventional image-based models typically assume simplified fluid dynamics, functional flow may also be influenced by blood cellular properties. We evaluated a multimodal neural network framework that conditions angiographic representations on hemorheological surrogate parameters derived directly from routine complete blood count (CBC) data.