Visual estimation of guidewire contact forces by integrating multi-scale features and region mask.

During percutaneous coronary intervention (PCI), the contact force between the guidewire and the vessel wall is a key mechanical factor in vessel injury, yet it cannot be measured directly with conventional imaging. Although sensor-based methods measure the contact force directly, they increase both the complexity and the cost of the instrumentation. A sensorless guidewire contact force estimation method is therefore proposed, in which multi-scale feature extraction is combined with region mask mean pooling to learn an end-to-end mapping from conventional planar images to the contact force in each of the four predefined contact regions. The proposed method is evaluated on a dataset of 343 samples acquired on a 3D-printed vessel phantom, achieving a mean absolute error (MAE) of 0.861 g and a root mean square error (RMSE) of 1.036 g. Ablation experiments show that the region mask reduces the MAE by 79% relative to the identical architecture without the region mask. Requiring neither additional sensing hardware nor complex physical modeling, the method estimates the contact force from visual information alone. These results support the feasibility of vision-based sensorless contact force estimation and suggest a possible route toward force feedback in interventional procedures.