Modern surgical devices are full of innovations and provide plenty of functionalities. However, they only perform to their full potential if they are properly configured. Only a small subset of surgical device functionalities is used due to the high complexity of today's device systems and the omnipresent situation that only the circulating nurses can (re-)configure non-sterile devices. Hence, there is a huge need for safe and effective assistance supporting the operating room (OR) staff to continuously work with the potentially best device setting to increase patient's safety and clinical outcome. Therefore, we propose a concept for situation-aware parameter recommendations and automatic (re-)configuration. Free access to adequate data is a prerequisite to extract knowledge for assistive systems and to provide situation awareness during execution. Thus, manufacturer-independent medical device interoperability is a basic requirement. Consequently, we use the new IEEE 11073 Service-oriented Device Connectivity (SDC) standards family, including Device Specializations. As part of the developing committee we introduce the idea and concept behind Device Specializations and highlight their use for assistive systems in the domain of minimal invasive surgery. To ensure safety and effectiveness of the assistive functionalities, our concept enforces deterministic rules providing a predictable and approvable behavior. We demonstrate our concept by the use case of a smart surgical double roller pump using an interpreter-based Rule Execution Engine.

Medical therapy devices need to interact with the patient to achieve a desired clinical result. Therefore, the patient is part of the final therapy system. But during the development and validation phase of medical devices the patient can not be involved in the design process. A replacement for the patient, or rather the body part which interacts with the therapy device, is needed. Simulators can be used to solve this problem. This contribution introduces the concept of a Hardware-In-The-Loop (HIL) simulator which is designed to be usable in a wide range of applications in the field of medical technology. HIL simulators consist of a software model of the simulated process and a physical interface to the therapy device. Actuators are used to influence the states of the physical interface and sensors measure the interaction between the HIL simulator and the therapy device. As a first application, the process of a minimally invasive surgery (MIS) was chosen. Insight to the process of the MIS was elaborated at the example of an arthroscopy. A software model of the knee, representing the operation area, was developed. A prototype was designed and different types of actuators were implemented. An over-actuated design was chosen to ensure a high flexibility. Control oriented models of the used actuators were derived. Finally, different actuator control strategies were developed, implemented and validated successfully.

The interconnection of therapy devices and the associated intelligent usage of available information is a key element for future improvements of medical therapies. Today, the success of an operation is determined by the quality of the therapy devices in connection with the individual knowledge and skills of the surgeon. A novel approach for the automatic, data based enhancement of a modern surgical technique is developed by a research association of clinical, academic and industrial partners. The concept of the project AFluCoMIS is applied to the minimally invasive surgery. The basic idea can be summarized as an increased usage of available data during an intervention. This information can be used to build an assistance system for the clinical staff which supports the surgeon with automatic device configuration, reference value settings and provides monitoring and safety features. A second innovation in the concept is derived from a postoperative correlation analysis between the course of the intervention and the clinical result. Knowledge can be gained from statistical data and improve following interventions. As a result, experience from past interventions is automatically made available to all surgeons by applying the derived parameters to the device. The combination of the surgeon's individual experience and automatically accumulated knowledge improves continuously the conditions for a successful intervention.