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.

Methods

Retrospective data from 179 patients (256 left anterior descending [LAD] videos) with matched invasive FFR were analyzed. To incorporate cellular flow tendencies without specialized assays, routine CBC parameters were configured into a 5-parameter Blood Fluidity Model (BFM) comprising gender, baseline mean platelet volume (MPV), and three engineered surrogate metrics: Erythrocyte Fragility Index (red blood cell distribution width [RDW] × mean corpuscular hemoglobin concentration [MCHC]), Cell-Free Hemoglobin Index (hemoglobin [HGB]—(hematocrit [HCT]/3)), and Erythrocyte-Platelet Volume Balance (HCT/plateletcrit [PCT]). A Feature-wise Linear Modulation (FiLM) architecture was used to modulate three-dimensional convolutional neural network (3D-CNN) spatiotemporal features extracted from angiography videos. Evaluation relied on patient-grouped 5-fold cross-validation and an independent test set (36 patients).