Vision-Based Surgical Tool Pose Estimation for the da Vinci® Robotic Surgical System

MUTUAL: Towards Holistic Sensing and Inference in the Operating Room

Embodied AI (E-AI) in the form of intelligent surgical robotics and other agents is calling for data platforms to facilitate its development and deployment. In this work, we present a cross-platform multimodal data recording and streaming software, MUTUAL, successfully deployed on two clinical studies, along with its ROS 2 distributed adaptation, MUTUAL-ROS 2. We describe and compare the two implementations of MUTUAL through their recording performance under different settings. MUTUAL offers robust recording performance at target configurations for multiple modalities, including video, audio, and live expert commentary. While this recording performance is not matched by MUTUAL-ROS 2, we demonstrate its advantages related to real-time streaming capabilities for AI inference and more horizontal scalability, key aspects for E-AI systems in the operating room. Our findings demonstrate that the baseline MUTUAL is well-suited for data curation and offline analysis, whereas MUTUAL-ROS 2, should match the recording reliability of the baseline system under a fully distributed manner where modalities are handled independently by edge computing devices. These insights are critical for advancing the integration of E-AI in surgical practice, ensuring that data infrastructure can support both robust recording and real-time processing needs.

Robot-assisted versus open hepatectomy for liver tumors: Systematic review and meta-analysis

BACKGROUND

This meta-analysis was conducted to evaluate the effectiveness and safety of robot-assisted hepatectomy (RAH) versus open hepatectomy (OH) for liver tumors (LT).

METHODS

A computer-based literature search was conducted to identify all randomized or nonrandomized controlled trials of RAH and OH in the treatment of LT from January 2000 to July 2022. Study-specific effect sizes and their 95% confidence intervals (CIs) were combined to calculate the pooled values, using a fixed-effects or random-effects model.

RESULTS

Eight studies were included, with a combined total of 1079 patients. Compared with the OH group, the RAH group was found to involve less blood loss (standardized mean difference [SMD] = -152.52 mL; 95% confidence interval [CI] = -266.85 to 38.18; p = 0.009), shorter hospital stay (SMD = -2.79; 95% CI = -4.19 to -1.40; p < 0.001), a lower rate of postoperative complications (odds ratio [OR] =0.67; 95% CI = 0.47-0.95; p = 0.02), and a lower recurrence rate (OR = 0.42; 95% CI = 0.23-0.77; p = 0.005). However, operative time was longer in the RAH group than in the OH group (SMD = 70.55; 95% CI = 37.58-103.53; p < 0.001).

CONCLUSION

This systematic review shows that RAH is safe and feasible in the treatment of LT.

Camera-Robot Calibration for the da Vinci® Robotic Surgery System

The development of autonomous or semi-autonomous surgical robots stands to improve the performance of existing teleoperated equipment, but requires fine hand-eye calibration between the free-moving endoscopic camera and patient-side manipulator arms (PSMs). A novel method of solving this problem for the da Vinci® robotic surgical system and kinematically similar systems is presented. First, a series of image-processing and optical-tracking operations are performed to compute the coordinate transformation between the endoscopic camera view frame and an optical-tracking marker permanently affixed to the camera body. Then, the kinematic properties of the PSM are exploited to compute the coordinate transformation between the kinematic base frame of the PSM and an optical marker permanently affixed thereto. Using these transformations, it is then possible to compute the spatial relationship between the PSM and the endoscopic camera using only one tracker snapshot of the two markers. The effectiveness of this calibration is demonstrated by successfully guiding the PSM end effector to points of interest identified through the camera. Additional tests on a surgical task, namely grasping a surgical needle, are also performed to validate the proposed method. The resulting visually-guided robot positioning accuracy is better than the earlier hand-eye calibration results reported in the literature for the da Vinci® system, while supporting intraoperative update of the calibration and requiring only devices that are already commonly used in the surgical environment.