In intraocular microsurgery with minute operational scales, instruments pass through non-visible regions of the anterior segment, where robot-assisted surgery, which heavily relies on visual perception, fails to determine the instrument’s attitude relative to the eyeball. This compromises surgical flexibility, increases risks, and hinders autonomous surgery development. Therefore, a framework for predicting instrument trajectories in non-visible regions during robot-assisted microsurgery has been proposed to mitigate the risks of retinal and lens injuries caused by blind operations and enhance surgical procedures’ intelligence and autonomy. First, a lightweight reconstruction of the anterior segment environment is performed under controlled knowledge guidance to construct a global map. Second, the tip position of the surgical instrument is detected through multi-sensor fusion, enabling the perception of instrument-environment interactions under visual constraints. Based on this, a long short-term spatiotemporal aggregation algorithm for instrument trajectory prediction is proposed, which enhances surgical safety by providing high-precision predictions of the instrument tip’s motion trajectory. Experiments show that the framework achieved a 0.0435 mm average prediction error in non-visible regions, corresponding to 0.03% of the region in a single dimension and 7.25% of the surgical instrument’s diameter. This significantly enhances the precision of robot-assisted surgery under visual constraints and provides robust technical support for safe, intelligent, and autonomous intraocular robotic surgery.

This research addresses the challenge of achieving high-precision tracking of time-varying trajectories under nonlinear disturbances and motion constraints in microsurgical robots. A hybrid control framework integrating fuzzy adaptive sliding mode control with radial basis function neural networks is proposed. This framework dynamically adjusts the sliding mode gain to suppress high-frequency jitter and compensate for unmodeled disturbances such as joint friction and tissue contact forces. Experiments conducted on a self-developed microscopic ophthalmic robot platform demonstrated that the trajectory tracking error was reduced to 1.1 μm, representing improvements of 85.9%, 76.1%, and 66.7% compared to PID control, sliding mode control and non-singular fast terminal sliding mode control respectively. The tracking delay was 19 milliseconds. In experiments on living pigs with central retinal artery occlusion, the system successfully performed intravascular injection, with a maximum error of 3.97 μm. This solution, through optimization via fuzzy logic and neural networks, achieves micron-level precision and robustness, effectively solving high-frequency control noise and low-frequency environmental disturbances, ensuring both the accuracy and safety of the microsurgical robot.

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

Pelvic fractures are one of the most serious traumas in orthopedics, and the technical proficiency and expertise of the surgical team strongly influence the quality of reduction results. With the advancement of information technology and robotics, robot-assisted pelvic fracture reduction surgery is expected to reduce the impact caused by inexperienced doctors and improve the accuracy and stability of pelvic reduction. However, this requires the robot to detect key surgeon actions from time-series data, enabling the robot to independently perceive the surgical status, predict the surgeon's intentions, assess the demonstrated level of professional competence, and assess the progress of the surgery. Therefore, a multi-task deep learning neural network architecture is proposed, which incorporates Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) along with tri-modality fusion and feature extraction techniques. The proposed framework aims to achieve key action detection in closed reduction operations for pelvic fractures. Subsequently, a trimodal fine-grained dataset was constructed, wherein 29, 32, and 14 labels were marked on flexion, position, and pressure data for 14 key closed reduction actions. The experimental results show that the correct detection rate of closed reduction actions is 92.3 %, significantly higher than the commonly used recognition algorithms. This work provides a method for the robot to learn the surgeon's professional knowledge, provides the basis for the operation's motion perception, and contributes to the autonomy of the robot-assisted closed reduction surgery of pelvic fractures.

Pelvic fractures are one of the most serious traumas in orthopedic care, and reduction during routine surgery is a significant challenge. Because there are so many vital organs, blood vessels, and nerves around the pelvis, and the reduction force is large, the operational requirements for the surgeon are extremely strict and require extensive experience and surgical skills. This article proposes a method for collecting and digitizing doctors’ reduction movements, which aims to help intelligent devices recognize surgeons’ reduction actions and provides a means to learn from expert experience to improve the accuracy of surgery. First, the convolutional bidirectional long short-term memory algorithm with multilayer cross-fused features is proposed. It extracts time and spatial correlations between multimodal data in a hierarchical manner. Second, discrete dynamic motion primitives are adopted for mapping the surgeon's palm movement trajectory. Finally, this article constructs a data acquisition platform and collects data from surgeons with varying proficiency in closed reduction. Experiment results show that the closed reduction action recognition accuracy is 99% and posture recognition accuracy is 95.5%. The recognition algorithm proposed by this article is significantly higher than the commonly used algorithms in terms of Accuracy, Precision, Recall, and F1-Score. This article provides methods and means for the digitization of surgical expertise and transfers learning for robot-assisted surgery.