Surgeonix: Smart AI-enabled Robotic Arm for Surgery Tool Detection and Handover Automation

Object identification, a critical technique in computer vision, has greatly improved several industries, notably medical and surgical applications. Our work seeks to improve surgical procedures by utilizing sophisticated object identification algorithms such as YOLOv8 with CNN, YOLOv9 with CNN, and YOLOv10 with CNN. These algorithms were chosen for their high speed, accuracy, and efficiency, which are critical for real-time tool identification in dynamic surgical situations. The YOLOv8+CNN has given an mAP of 99.3% and accuracy of 93.61%, the YOLOv9+CNN has provided a mAP score of 99.4% and accuracy of 95.54% and the YOLOv10+CNN has given an mAP of 99.1% with the accuracy of 91.96%. Within the operation room, the robotic arm will be used as an assistant to the surgeons. Our research tackles major issues in traditional tool exchange systems, notably during emergencies and in areas of resource-constrained circumstances, by creating a robotic surgical assistant capable of autonomous tool detection and delivery. By automating tool exchange procedures, the system hopes to eliminate surgical mistakes, increase productivity, and improve patient safety. It also assists surgical teams in underprivileged areas by ensuring timely and efficient tool handling. The project will result in a strong, dependable, and scalable system that will greatly improve surgical care quality and patient outcomes by integrating modern AI and robotics technologies.