A Vision-Based Method For Estimating Contact Forces In Intracardiac Catheters

Atrial fibrillation is a kind of cardiac arrhythmia in which the electrical signals of the heart are uncoordinated. The prevalence of this disease is increasing globally and the curative treatment for this problem is catheter ablation therapy. The adequate contact force between the tip of a catheter and cardiac tissue significantly can increase the efficiency and sustainability of the mentioned treatment. To satisfy the need of cardiologists for haptic feedback during the surgery and increase the efficacy of ablation therapy, in this paper a sensorfree method is proposed in such a way that the system is able to estimate the force directly from image data. To this end, a mechanical setup is designed and implemented to imitate the real ablation procedure. A novel vision-based feature extraction algorithm is also proposed to obtain catheter’s bending variations obtained from the setup. Using the extracted feature, machine learning algorithms are responsible of estimating the forces. The results revealed ${MAE \lt }0.0041$ and the proposed system is able to estimate the force precisely.