Endoscopic Submucosal Dissection (ESD) is a surgical procedure employed in the treatment of early-stage gastrointestinal cancers [1, 2]. This procedure entails a complex series of dissection maneuvers that require significant skill to determine the dissection zone. In traditional ESD, a transparent cap is employed to retract lesions, which can often obscure the view of the submucosal layer and lead to an incomplete dissection zone. Conversely, our robot-assisted ESD [3] offers better visualization of the submucosal layer, resulting in a more completed dissection zone by utilizing robotic instruments that enable independent control over retraction and dissection. Achieving successful submucosal dissection requires the careful excision of the lesion or mucosal layer along with the complete submucosal layer while ensuring that both the underlying muscular layer and the mucosal surface remain unharmed. If the electric knife inadvertently contacts tissue outside the designated dissection area, it can lead to damage to the muscle layer, increasing the risk of perforations. Such complications not only elevate the surgical risks but can also complicate the patient's recovery. Therefore, it is imperative to maintain a precise dissection zone during endoscopic procedures. Effective guidance can help ensure that surgeons are adept at identifying and adhering to appropriate dissection boundaries and enhance the overall safety of endoscopic submucosal dissection (ESD).

With the development of the internet, recommending interesting products to users has become a highly valuable research topic for businesses. Recommendation systems play a crucial role in addressing this issue. To prevent the leakage of each user's (client's) private data, Federated Recommendation Systems (FedRec) have been proposed and widely used. However, extensive research has shown that FedRec suffers from security issues such as data privacy leakage, and it is challenging to train effective models with FedRec when each client only holds interaction information for a single user. To address these two problems, this paper proposes a new privacy-preserving recommendation system (PRSI), which includes a preprocessing module and two main phases. The preprocessing module employs split vectors and fake interaction items to protect clients' interaction information and recommendation results. The two main phases are: (1) the collection of interaction information and (2) the sending of recommendation results. In the interaction information collection phase, each client uses the preprocessing module and random communication methods (according to the designed interactive protocol) to protect their ID information and IP addresses. In the recommendation results sending phase, the central server uses the preprocessing module and triplets to distribute recommendation results to each client under secure conditions, following the designed interactive protocol. Finally, we conducted multiple sets of experiments to verify the security, accuracy, and communication cost of the proposed method.

Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, and improving surgical training. Nevertheless, predicting these trajectories is challenging due to variable tumor margins and dynamic visual conditions. To address this issue, we create the ESD Trajectory and Confidence Map-based Safety Margin (ETSM) dataset with $1849$ short clips, focusing on submucosal dissection with a dual-arm robotic system. We also introduce a framework that combines optimal dissection trajectory prediction with a confidence map-based safety margin, providing a more secure and intelligent decision-making tool to minimize surgical risks for ESD procedures. Additionally, we propose the Regression-based Confidence Map Prediction Network (RCMNet), which utilizes a regression approach to predict confidence maps for dissection areas, thereby delineating various levels of safety margins. We evaluate our RCMNet using three distinct experimental setups: in-domain evaluation, robustness assessment, and out-of-domain evaluation. Experimental results show that our approach excels in the confidence map-based safety margin prediction task, achieving a mean absolute error (MAE) of only $3.18$. To the best of our knowledge, this is the first study to apply a regression approach for visual guidance concerning delineating varying safety levels of dissection areas. Our approach bridges gaps in current research by improving prediction accuracy and enhancing the safety of the dissection process, showing great clinical significance in practice.