Spy: A Flexible Code Profiling Framework

Code profiling is an essential activity to increase software quality. It is commonly employed in a wide variety of tasks, such as supporting program comprehension, determining execution bottlenecks, and assessing code coverage by unit tests. Spy is an innovative framework to easily build profilers and visualize profiling information. The profiling information is obtained by inserting dedicated code before or after method execution. The gathered profiling information is structured in line with the application structure in terms of packages, classes, and methods. Spy has been instantiated on four occasions so far. We created profilers dedicated to test coverage, time execution, type feedback, and profiling evolution across version. We also integrated Spy in the Pharo IDE. Spy has been implemented in the Pharo Smalltalk programming language and is available under the MIT license.

Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study

We evaluated the concurrent and predictive validity of a novel robotic surgery simulator in a prospective, randomized study.

Robotic and laparoscopic high extended pelvic lymph node dissection during radical cystectomy: technique and outcomes

With the increasing use of laparoscopic and robotic radical cystectomy (RC), there are perceived concerns about the adequacy of lymph node dissection (LND).

Cost-effectiveness of open versus laparoscopic versus robotic-assisted laparoscopic cystectomy and urinary diversion

To provide insight into the recently published cost comparisons in the context of open, laparoscopic, and robotic-assisted laparoscopic radical cystectomy and to demonstrate the complexity of such economic analyses.

Face, content and construct validity of a novel robotic surgery simulator

We evaluated the face, content and construct validity of the novel da Vinci® Skills Simulator™ using the da Vinci Si™ Surgeon Console as the surgeon interface.

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

Validation of a novel robotic-assisted partial nephrectomy surgical training model

What's known on the subject? and What does the study add? One area of particular growth for robotic surgery has been partial nephrectomy. Despite a perceived notion that robotic-assisted partial nephrectomy is more easily adaptable compared to laparoscopic partial nephrectomy, there is nonetheless an associated learning curve. Validated training models with a corresponding assessment method for robotic-assisted partial nephrectomy were previously unavailable. We have designed and validated a RAPN surgical model appropriate for resident and fellow training.