Robotic surgery is nowadays one of the most important changes in modern medicine, as it is more precise, less invasive, and has a shorter recovery time. This review addresses the integration of computer vision into robotic-assisted surgery, with a case study of the Da Vinci Surgical System. The equipment consists of robotic arms controlled by a surgeon’s console, which provides high-definition 3D imagery and superior motion control. Computer vision improves depth perception, real-time instrument tracking, and surgical image processing to guide instruments more precisely, helping to identify complex anatomical structures. This is a review-based study that uses over 50 peer-reviewed articles, manufacturer technical specifications, and published clinical performance data. No original experimental or observational patient data that was gathered. Simulation settings of synthetic tissue models, validated kinematics and multi-specialty clinical case reports are mentioned in the referenced works. The existing drawbacks are associated with cost, low automation, and human-based operation. Artificial intelligence, machine learning, and autonomous functions are likely to be developed further, and current feasibility is limited to AI-assisted imaging, anatomical landmark detection and simple camera automation. Full autonomy in surgery is still in the research phase and is expected to be adopted in 8 to 10 years. Developments in imaging, system assessment, and clinical studies reveal that computer vision will continue to change robotic surgery in every corner of the globe.
Chowdary et al. (Mon,) studied this question.