Robotic-Assisted Minimally Invasive Surgery Systems

Abstract

Robotic-assisted minimally invasive surgery (RAMIS) has transformed surgical practice by providing enhanced dexterity, tremor filtration, and 3D visualisation that overcome the ergonomic and precision limitations of conventional laparoscopy. This study developed and evaluated a novel compact surgical robot -- the FlexArm system -- featuring a 6-degree-of-freedom (6-DOF) cable-driven continuum manipulator with integrated force sensing, real-time AI-assisted tissue classification, and autonomous suturing capability. The FlexArm was benchmarked against the da Vinci Si standard and conventional laparoscopy across five standardised surgical tasks: peg transfer, pattern cutting, intracorporeal suturing, dissection accuracy, and tissue retraction force control. In a 48-participant study (16 expert surgeons, 16 intermediate, 16 novice), FlexArm achieved task completion times within 8.4% of the da Vinci Si (p = 0.12) while providing 42% lower instrument cost and 68% smaller console footprint. Force-sensing feedback reduced tissue damage events by 54.2% versus the da Vinci Si (p < 0.001) and 78.4% versus laparoscopy (p < 0.001). The AI tissue classifier (ResNet-18 on intraoperative video) distinguished five tissue types (fat, muscle, nerve, vessel, tumour) with 94.2% accuracy in real-time (28 fps). Autonomous suturing achieved 92.4% suture placement accuracy within 1 mm of the target position. These results establish FlexArm as a cost-effective, AI-enhanced RAMIS platform with force-sensing safety advantages over current systems.