OR black box and surgical control tower: Recording and streaming data and analytics to improve surgical care.

Effective and safe surgery results from a complex sociotechnical process prone to human error. Acquiring large amount of data on surgical care and modelling the process of surgery with artificial intelligence's computational methods could shed lights on system strengths and limitations and enable computer-based smart assistance. With this vision in mind, surgeons and computer scientists have joined forces in a novel discipline called Surgical Data Science. In this regard, operating room (OR) black boxes and surgical control towers are being developed to systematically capture comprehensive data on surgical procedures and to oversee and assist during operating rooms activities, respectively. Most of the early Surgical Data Science works have focused on understanding risks and resilience factors affecting surgical safety, the context and workflow of procedures, and team behaviors. These pioneering efforts in sensing and analyzing surgical activities, together with the advent of precise robotic actuators, bring surgery on the verge of a fourth revolution characterized by smart assistance in perceptual, cognitive and physical tasks. Barriers to implement this vision exist, but the surgical-technical partnerships set by ambitious efforts such as the OR black box and the surgical control tower are working to overcome these roadblocks and translate the vision and early works described in the manuscript into value for patients, surgeons and health systems.

A boosted segmentation method for surgical workflow analysis.

As demands on hospital efficiency increase, there is a stronger need for automatic analysis, recovery, and modification of surgical workflows. Even though most of the previous work has dealt with higher level and hospital-wide workflow including issues like document management, workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports. In this paper we propose an approach to segment the surgical workflow into phases based on temporal synchronization of multidimensional state vectors. Our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. The discriminative power of each instrument in regard to each phase is estimated using AdaBoost. A boosted version of the Dynamic Time Warping (DTW) algorithm is used to create a surgical reference model and to segment a newly observed surgery. Full cross-validation on ten surgeries is performed and the method is compared to standard DTW and to Hidden Markov Models.