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. Description An autonomous robotic strategy allows laparoscopic soft tissue surgery in end-to-end anastomosis of the small bowel.

Autonomous robotic laparoscopic surgery for intestinal anastomosis

Objective: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. Methods: An article search was performed on 5 bibliographic databases with the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. Results: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. Conclusion: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. Significance: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.

/pdf/gesture-recognition-in-robotic-surgery-a-review-2sapc267xa.pdf

Gesture Recognition in Robotic Surgery: A Review

A systematic review on artificial intelligence in robot-assisted surgery.

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis. We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling and history-dependent effects between joints, we analyze data from sampled trajectories and consider 13 approaches to modeling. These models include linear regression and LSTM recurrent neural networks, each with varying temporal window length to provide compensatory feedback. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of the physical robot from 2.96 mm to 0.65 mm. Results on the execution of open-loop trajectories of the FLS peg transfer surgeon training task suggest that the best model increases success rate from 39.4 % to 96.7 %, producing performance comparable to that of an expert surgical resident. Supplementary materials, including code and 3D-printable models, are available at this https URL

Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis. We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling and history-dependent effects between joints, we analyze data from sampled trajectories and consider 13 approaches to modeling. These models include linear regression and LSTM recurrent neural networks, each with varying temporal window length to provide compensatory feedback. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of physical robot from 2.96 mm to 0.65 mm. Results on the execution of open-loop trajectories of the FLS peg transfer surgeon training task suggest that the best model increases success rate from 39.4% to 96.7%, producing performance comparable to that of an expert surgical resident. Supplementary materials, including code and 3D-printable models, are available at https://sites.google.com/berkeley.edu/surgical-calibration.

Efficiently Calibrating Cable-Driven Surgical Robots With RGBD Fiducial Sensing and Recurrent Neural Networks

Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/options in a semi-supervised manner. We present Motion2Vec, an algorithm that learns a deep embedding feature space from video observations by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while pushed away from randomly sampled images of other segments, while respecting the temporal ordering of the images. The embeddings are iteratively segmented with a recurrent neural network for a given parametrization of the embedding space after pre-training the Siamese network. We only use a small set of labeled video segments to semantically align the embedding space and assign pseudo-labels to the remaining unlabeled data by inference on the learned model parameters. We demonstrate the use of this representation to imitate surgical suturing kinematic motions from publicly available videos of the JIGSAWS dataset. Results give 85.5% segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose imitation gives 0.94 centimeter error in position per observation on the test set. Videos, code and data are available at: https://sites.google.com/view/motion2vec

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

This paper presents a framework to learn the sequential structure in the demonstrations for robot imitation learning. We first present a family of task-parameterized hidden semi-Markov models that ...

Sequential robot imitation learning from observations

Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/sub-goals/options in a semi-supervised manner. We present Motion2Vec, an algorithm that learns a deep embedding feature space from video observations by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while pushed away from randomly sampled images of other segments, while respecting the temporal ordering of the images. The embeddings are iteratively segmented with a recurrent neural network for a given parametrization of the embedding space after pre-training the Siamese network. We only use a small set of labeled video segments to semantically align the embedding space and assign pseudo-labels to the remaining unlabeled data by inference on the learned model parameters. We demonstrate the use of this representation to imitate surgical suturing motions from publicly available videos of the JIGSAWS dataset. Results give 85.5 % segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose imitation gives 0.94 centimeter error in position per observation on the test set. Videos, code and data are available at this https URL

To combat climate change, we need to change user transportation behavior to be less carbon intensive. Prior work on motivating this behavior change has been predominantly qualitative and lacks comparison. This makes it challenging to determine which interventions should be deployed at scale. The behavior change community needs a process to compare interventions against each other in pilot studies before committing deployment resources. We perform the first quantitative comparison, to our knowledge, of behavior change strategies in the transportation behavior domain. Since this is a pilot with a limited recruitment budget, we design a Randomized Controlled Trial (RCT) using an open source platform. We assign 41 users to three mobile applications: Emotion, Information, Control. The RCT allows us to draw statistically valid inferences that can suggest future avenues for larger-scale studies. We found that Emotion resulted in greater engagement with the application (p=0.006, 0.035, 0.031, 0.040) while Information improved the sustainability of travel behavior (p = 0.043). These exploratory statistical results can motivate the design of future studies to further explore combinations of these approaches for sustainable transportation behavior.

https://dl.acm.org/doi/pdf/10.1145/3360322.3360871

TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications

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Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Sequential robot imitation learning from observations

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications