Force-Sensing-Integrated Flexible Tool for Precise Lesion Resection in Minimally Invasive Breast Surgery

Background: Minimally invasive breast surgery often suffers from incomplete lesion removal due to poor ultrasound visualisation. Methods: Given the difference in hardness between lesions and adipose tissue, this study proposes a method to assess tissue boundaries by measuring cutting force. Based on the Intact Lesion Excision System, a breast biopsy tool capable of tissue grasping and cutting, a cutting force detection algorithm was developed. This algorithm establishes a mechanical relationship among DILES deformation, actuation tension, and actual cutting force, and estimates tissue type by measuring cutting resistance. Results: Experimental results show that the tool can detect cutting forces lower than 0.05 N. Force prediction achieved an RMSE of 0.0116 N and an R² of 0.892. In simulated lesion excision, force-sensing guidance improved the complete resection rate from 40% (visual alone) to 90% (visual plus force feedback). Conclusions: This force-sensing approach enhances lesion resection completeness and shows strong potential for clinical application.