Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeon’s skill and experience. Autonomous anastomosis is a challenging soft-tissue surgery task because it requires intricate imaging, tissue tracking, and surgical planning techniques, as well as a precise execution via highly adaptable control strategies often in unstructured and deformable environments. In the laparoscopic setting, such surgeries are even more challenging because of the need for high maneuverability and repeatability under motion and vision constraints. Here we describe an enhanced autonomous strategy for laparoscopic soft tissue surgery and demonstrate robotic laparoscopic small bowel anastomosis in phantom and in vivo intestinal tissues. This enhanced autonomous strategy allows the operator to select among autonomously generated surgical plans and the robot executes a wide range of tasks independently. We then use our enhanced autonomous strategy to perform in vivo autonomous robotic laparoscopic surgery for intestinal anastomosis on porcine models over a 1-week survival period. We compared the anastomosis quality criteria—including needle placement corrections, suture spacing, suture bite size, completion time, lumen patency, and leak pressure—of the developed autonomous system, manual laparoscopic surgery, and robot-assisted surgery (RAS). Data from a phantom model indicate that our system outperforms expert surgeons’ manual technique and RAS technique in terms of consistency and accuracy. This was also replicated in the in vivo model. These results demonstrate that surgical robots exhibiting high levels of autonomy have the potential to improve consistency, patient outcomes, and access to a standard surgical technique.

The state of polarization (SOP) is the key characteristic of light; its evaluation is typically based on measurements of the Stokes parameters. However, conventional approaches often have bulky and complex optical systems with long acquisition times. In this Letter, an ultracompact polarimeter based on dielectric metasurfaces is proposed. The pixel of the proposed metasuface has four areas; each area acts not only as a polarization separator, but also a flat focusing lens. It can acquire a certain polarization component of the incident light and focus the component on the receiver. Therefore, the Stokes parameters of the incident light can be immediately determined through the data from the receiver, fully describing its SOP. The incident waves with different SOPs are simulated to demonstrate that the reconstructed Stokes parameters are in good agreement with the original ones.

Producing spatial transformations that are diffeomorphic is a key goal in deformable image registration. As a diffeomorphic transformation should have positive Jacobian determinant $$\vert J\vert $$| J | everywhere, the number of pixels (2D) or voxels (3D) with $$\vert J\vert <0$$| J | < 0 has been used to test for diffeomorphism and also to measure the irregularity of the transformation. For digital transformations, $$\vert J\vert $$| J | is commonly approximated using a central difference, but this strategy can yield positive $$\vert J\vert $$| J | ’s for transformations that are clearly not diffeomorphic—even at the pixel or voxel resolution level. To show this, we first investigate the geometric meaning of different finite difference approximations of $$\vert J\vert $$| J | . We show that to determine if a deformation is diffeomorphic for digital images, the use of any individual finite difference approximation of $$\vert J\vert $$| J | is insufficient. We further demonstrate that for a 2D transformation, four unique finite difference approximations of $$\vert J\vert $$| J | ’s must be positive to ensure that the entire domain is invertible and free of folding at the pixel level. For a 3D transformation, ten unique finite differences approximations of $$\vert J\vert $$| J | ’s are required to be positive. Our proposed digital diffeomorphism criteria solves several errors inherent in the central difference approximation of $$\vert J\vert $$| J | and accurately detects non-diffeomorphic digital transformations. The source code of this work is available at https://github.com/yihao6/digital_diffeomorphism.

Retinal surgery is a challenging procedure requiring precise manipulation of the fragile retinal tissue, often at the scale of tens-of-micrometers. Its difficulty has motivated the development of robotic assistance platforms to enable precise motion, and more recently, novel sensors such as microscope integrated optical coherence tomography (OCT) for RGB-D view of the surgical workspace. The combination of these devices opens new possibilities for robotic automation of tasks such as subretinal injection (SI), a procedure that involves precise needle insertion into the retina for targeted drug delivery. Motivated by this opportunity, we develop a framework for autonomous needle navigation during SI. We develop a system which enables the surgeon to specify waypoint goals in the microscope and OCT views, and the system autonomously navigates the needle to the desired subretinal space in real-time. Our system integrates OCT and microscope images with convolutional neural networks (CNNs) to automatically segment the surgical tool and retinal tissue boundaries, and model predictive control that generates optimal trajectories that respect kinematic constraints to ensure patient safety. We validate our system by demonstrating 30 successful SI trials on pig eyes. Preliminary comparisons to a human operator in robot-assisted mode highlight the enhanced safety and performance of our system.

Background: Tissue temperature monitoring during cutaneous laser therapy can lead to safer and more effective treatments. In this study, we investigate the use of speckle variance optical coherence tomography (svOCT) to monitor real-time temperature changes in the excised human skin tissue sample during laser irradiation. Methods: To accomplish this, we combined the pulse laser system with a reference-based svOCT system. To calibrate the svOCT, the ex-vivo human skin samples from three individuals with tissues collected from the arm, face, and back were heated with 1-degree increments. Additionally, linear regression was used to extract and evaluate the linear relationship between the temperature and normalized speckle variance value. Experiments were conducted on excised human skin sample to monitor the temperature change during laser therapy with a svOCT system. Thermal modeling of ex-vivo human skin was used to numerically simulate the laser-tissue interaction and estimate the thermal diffusion and peak temperature of the tissue during the laser treatment. Results and Conclusion: These results showed that normalized speckle variance had a linear relationship with the tissue temperature before the onset of tissue coagulation (52°) and we were able to measure the rapid increase of the tissue temperature during laser therapy. The result of the experiment is also in good agreement with the numerical simulation result that estimated the laser-induced peak temperature and thermal relaxation time.