Toward increased autonomy in the surgical OR: needs, requests, and expectations.

BACKGROUND: The current trend in surgery toward further trauma reduction inevitably leads to increased technological complexity. It must be assumed that this situation will not stay under the sole control of surgeons; mechanical systems will assist them. Certain segments of the work flow will likely have to be taken over by a machine in an automatized or autonomous mode. METHODS: In addition to the analysis of our own surgical practice, a literature search of the Medline database was performed to identify important aspects, methods, and technologies for increased operating room (OR) autonomy. RESULTS: Robotic surgical systems can help to increase OR autonomy by camera control, application of intelligent instruments, and even accomplishment of automated surgical procedures. However, the important step from simple task execution to autonomous decision making is difficult to realize. Another important aspect is the adaption of the general technical OR environment. This includes adaptive OR setting and context-adaptive interfaces, automated tool arrangement, and optimal visualization. Finally, integration of peri- and intraoperative data consisting of electronic patient record, OR documentation and logistics, medical imaging, and patient surveillance data could increase autonomy. CONCLUSIONS: To gain autonomy in the OR, a variety of assistance systems and methodologies need to be incorporated that endorse the surgeon autonomously as a first step toward the vision of cognitive surgery. Thus, we require establishment of model-based surgery and integration of procedural tasks. Structured knowledge is therefore indispensable.

Selective automation and skill transfer in medical robotics: a demonstration on surgical knot-tying.

BACKGROUND: Transferring non-trivial human manipulation skills to robot systems is a challenging task. There have been a number of attempts to design research systems for skill transfer, but the level of the complexity of the actual skills transferable to the robot was rather limited, and delicate operations requiring a high dexterity and long action sequences with many sub-operations were impossible to transfer. METHODS: A novel approach to human-machine skill transfer for multi-arm robot systems is presented. The methodology capitalizes on the metaphor of 'scaffolded learning', which has gained widespread acceptance in psychology. The main idea is to formalize the superior knowledge of a teacher in a certain way to generate support for a trainee. In our case, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during 'learning by demonstration'. The actual skill generalization is then based on simulating fluid dynamics. RESULTS: The approach has been successfully evaluated in the medical domain for the delicate task of automated knot-tying for suturing with standard surgical instruments and a realistic minimally invasive robotic surgery system.