Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks.

Purpose: Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, or short, atomic units of activity such as pushing needle through tissue, whereas we also focus on recognizing higher-level maneuvers, such as suture throw. Maneuvers exhibit more complexity and variability than the gestures from which they are composed, however working at this granularity has the benefit of being consistent with existing training curricula.

Does a robotic surgery approach offer optimal ergonomics to gynecologic surgeons?: a comprehensive ergonomics survey study in gynecologic robotic surgery.

Objective: To better understand the ergonomics associated with robotic surgery including physical discomfort and symptoms, factors influencing symptom reporting, and robotic surgery systems components recommended to be improved.

Methods: The anonymous survey included 20 questions regarding demographics, systems, ergonomics, and physical symptoms and was completed by experienced robotic surgeons online through American Association of Gynecologic Laparoscopists (AAGL) and Society of Robotic Surgery (SRS).

Results: There were 289 (260 gynecology, 22 gynecology-oncology, and 7 urogynecology) gynecologic surgeon respondents regularly practicing robotic surgery.

Can a virtual reality surgical simulation training provide a self-driven and mentor-free skills learning? Investigation of the practical influence of the performance metrics from the virtual reality robotic surgery simulator on the skill learning and associated cognitive workloads.

Background: While it is often claimed that virtual reality (VR) training system can offer self-directed and mentor-free skill learning using the system's performance metrics (PM), no studies have yet provided evidence-based confirmation. This experimental study investigated what extent to which trainees achieved their self-learning with a current VR simulator and whether additional mentoring improved skill learning, skill transfer and cognitive workloads in robotic surgery simulation training.

Methods: Thirty-two surgical trainees were randomly assigned to either the Control-Group (CG) or Experiment-Group (EG).

Query-by-example surgical activity detection.

Purpose: Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for surgical activity segments of interest is limited by the need for manual pre-processing, which can be prohibitive at scale. We developed a content-based information retrieval method, query-by-example (QBE), to automatically detect activity segments within surgical data recordings of long duration that match a query.

Comparative assessment of physical and cognitive ergonomics associated with robotic and traditional laparoscopic surgeries.

Background: We conducted this study to investigate how physical and cognitive ergonomic workloads would differ between robotic and laparoscopic surgeries and whether any ergonomic differences would be related to surgeons' robotic surgery skill level. Our hypothesis is that the unique features in robotic surgery will demonstrate skill-related results both in substantially less physical and cognitive workload and uncompromised task performance.

Methods: Thirteen MIS surgeons were recruited for this institutional review board-approved study and divided into three groups based on their robotic surgery experiences: laparoscopy experts with no robotic experience, novices with no or little robotic experience, and robotic experts.