Augmented reality (AR) and robotic-assisted surgery (RAS) are both rapidly evolving technologies in recent years. RAS systems, such as the da Vinci® Surgical System, aim to improve surgical precision and dexterity, as well as access to minimally-invasive procedures, while AR provides an advanced interface to enhance user perception. Combining the features of both, AR-integrated RAS has become an appealing concept with increased interest among the academic community. In this paper, we review the existing literature about AR-integrated RAS. We discuss the hardware components, application paradigms and clinical relevance of the literature. The concept of AR-integrated RAS has been shown to be feasible for various procedures. Encouraging preliminary results include reduced sight diversion and improved situation awareness. Special techniques, e.g., activation-on-demand, are taken into consideration to address visual clutter of the AR interface and ensure that the system is fail-safe. Although AR-integrated RAS is not yet mature, we believe that if the current trend of development continues, it will soon demonstrate its clinical value.

We propose an adaptable framework for analyzing ultrasound (US) images quantitatively to provide computer-aided diagnosis using machine learning. Our preliminary clinical targets are hepatic steatosis, adenomyosis, and craniosynostosis. For steatosis and adenomyosis, we collected US studies from 288 and 88 patients, respectively, as well as their biopsy or magnetic resonanceconfirmed diagnosis. Radiologists identified a region of interest (ROI) on each image. We filtered the US images for various texture responses and use the pixel intensity distribution within each ROI as feature parameterizations. Our craniosynostosis dataset consisted of 22 CT-confirmed cases and 22 age-matched controls. One physician manually measured the vectors from the center of the skull to the outer cortex at every 10 deg for each image and we used the principal directions as shape features for parameterization. These parameters and the known diagnosis were used to train classifiers. Testing with cross-validation, we obtained 72.74% accuracy and 0.71 area under receiver operating characteristics curve for steatosis ($p<0.0001$), 77.27% and 0.77 for adenomyosis ($p<0.0001$), and 88.63% and 0.89 for craniosynostosis ($p=0.0006$). Our framework is able to detect a variety of diseases with high accuracy. We hope to include it as a routinely available support system in the clinic.

The purpose of our study was to determine if a textural analysis metric can be implemented to improve diagnosis of adenomyosis by ultrasound. We retrospectively identified 38 patients with a magnetic resonance imaging (MRI) diagnosis of uterine adenomyosis that also had a pelvic ultrasound within 6 months. We also identified 50 normal pelvic ultrasound examinations confirmed by a normal pelvic MRI within 6 months as a control group. A region of interest (ROI) was subsequently placed on the study population ultrasound image corresponding to the area of adenomyosis on MRI. An ROI was placed in the area of the junctional zone in the normal controls. The abnormal and normal ROIs were then compared against trained normal and abnormal distributions to determine the success rate, sensitivity, specificity, and negative and positive predictive values of our computer metric. The ultrasound reports performed before MRI were also reviewed to determine the radiologist correct/incorrect interpretation rate for comparison with our textural analysis metric. Using a training population of 50 normal ultrasound examinations (confirmed with a normal MRI) and 38 abnormal ultrasound examinations (MRI confirmed adenomyosis), we had an overall 75% (66/88 accurately diagnosed) success rate with a sensitivity, specificity, and negative and positive predictive values of 70%, 79%, 73%, and 76%, respectively (P < .0001). The sensitivity and false-negative rate of the initial ultrasound interpretation were 26% (10/38) and 74% (28/38), respectively.

Robot control algorithms often rely on measurements of robot joint velocities, which can be estimated by measuring the time between encoder edges. When encoder edges occur infrequently, such as at low velocities and/or with low resolution encoders, this measurement delay may affect the stability of closed-loop control. This is evident in both the joint position control and Cartesian impedance control of the da Vinci Research Kit (dVRK), which contains several low-resolution encoders. We present a hardware-based method that gives more frequent velocity updates and is not affected by common encoder imperfections such as non-uniform duty cycles and quadrature phase error. The proposed method measures the time between consecutive edges of the same type but, unlike prior methods, is implemented for the rising and falling edges of both channels. Additionally, it estimates acceleration to enable software compensation of the measurement delay. The method is shown to improve Cartesian impedance control of the dVRK.