image: Classification of the guidance image applied for surgical robot.
Credit: ©Science China Press
Recently, the National Science Review published an online review paper by Professor Lihai Zhang from the Orthopedics Department of Chinese PLA General Hospital. The paper, titled "Advances of Surgical Robotics: Image-Guided Classification and Application," summarizes the latest advances in image-guided methods for surgical robots over recent years. These advancements have enabled surgical robots to become more intelligent and efficient in clinical applications, providing new possibilities for precise surgical operations.
Significant achievements have been made in the application of surgical robots in minimally invasive surgeries. In recent years, thousands of surgical robot systems have been installed in hospitals worldwide, performing millions of surgeries. By integrating sensing, control, and execution units, surgical robots assist surgeons in completing precise and efficient operations, thereby reducing trauma, alleviating postoperative pain, and shortening recovery time. The ability of surgical robot systems to perceive their environment is crucial for achieving high autonomy and intelligent operation, with image processing technology being at the core of this environmental perception.
Traditional image-guided methods often rely on fixed equipment within the operating room. In contrast, modern surgical robot systems can acquire and process high-precision images in real-time during surgery, providing intuitive operational guidance. Through algorithms such as data augmentation, target segmentation, and instrument tracking, surgical robots can quickly and accurately understand the surgical environment and respond accordingly, thereby improving the efficiency and accuracy of surgeries.
In the review, the research team categorizes navigational images based on the method of data acquisition into direct and indirect images, and based on the method of target tracking into continuous, intermittent continuous, and non-continuous images. Based on these two dimensions, they establish a new classification system for the navigational images of surgical robots, introducing the main characteristics and application scenarios of each category. This classification also serves as a basis for dividing various surgical robot systems that have been applied clinically, summarizing the general rules for the application of navigational images in surgical robots. This will provide guidance for developing more advanced surgical robot systems.
Despite significant achievements, image-guided technology for surgical robots still faces many challenges. Future research directions include image enhancement and surgical scene reconstruction, high-fidelity surgical simulation and intelligent planning, multimodal image registration and accurate localization for deformable tissues, and augmented reality-enhanced navigation. The review concludes with an outlook on the future development of surgical robot technology, aiming to enhance and surpass human surgical capabilities through improved intelligence and autonomy of surgical robots.
This work was supported by the National Natural Science Foundation of China. Professor Lihai Zhang from Chinese PLA General Hospital is the corresponding author, with Professor Changsheng Li from Beijing Institute of Technology, Dr. Gongzi Zhang from Chinese PLA General Hospital, and Dr. Baoliang Zhao from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, as co-first authors.
Journal
National Science Review