Privacy-proof Live Surgery Streaming: Development and Validation of a Low-cost, Real-time Robotic Surgery Anonymization Algorithm.

Objective: Develop a pioneer surgical anonymization algorithm for reliable and accurate real-time removal of out-of-body images validated across various robotic platforms.

Background: The use of surgical video data has become a common practice in enhancing research and training. Video sharing requires complete anonymization, which, in the case of endoscopic surgery, entails the removal of all nonsurgical video frames where the endoscope can record the patient or operating room staff.

Surgical Phase Duration in Robot-Assisted Partial Nephrectomy: A Surgical Data Science Exploration for Clinical Relevance.

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback, and teaching. The phase duration itself and its correlation with clinical parameters at diagnosis have not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet.

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery.

Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual three-dimensional (3D) models. Apart from correct model alignment and deformation, not all instruments are clearly visible in AR. Superimposition of a 3D model on top of the surgical stream, including the instruments, can result in a potentially hazardous surgical situation.

Multicentric exploration of tool annotation in robotic surgery: lessons learned when starting a surgical artificial intelligence project.

Background: Artificial intelligence (AI) holds tremendous potential to reduce surgical risks and improve surgical assessment. Machine learning, a subfield of AI, can be used to analyze surgical video and imaging data. Manual annotations provide veracity about the desired target features.