A Situational Awareness Enhancing System for Minimally Invasive Surgery Training

Abstract

Minimally Invasive Surgery (MIS) is a surgical technique involving small incisions performed by an endoscope and several long, thin instruments. Because of its minimally invasive nature, MIS minimizes complications and speeds up recovery time compared to the traditional surgery. Unfortunately, from a surgeon's perspective, MIS is much more challenging than conventional surgery. Because the limited vision and sensing feedbacks, MIS a difficult skill for medical students and residents to master.There has been some research on the effectiveness of different kinds of training and guidance. Surgical simulation is increasingly perceived as a valuable addition to traditional medical training methods, although most existing simulators have limitations stemming from either a lack of objective performance assessment or an insufficient relation to the operating room reality.The objective of this research is to design and realize a novel prototype that advances the state of the art in surgical training, assessment, and guidance for MIS. The prototype features micro-sensors embedded into the instruments employed for simulation training. The system provides multiple training scenarios, a high fidelity training environment, repeatable, structured exercises, and objective performance assessment capabilities.The proposed Situational Awareness Enhancing System (SAES) uses a unified framework incorporating perception, comprehension, and projection software modules that provide feedback during the exercises and enable evaluation of the training procedure.A multiple sensor data fusion method was developed to help surgeons efficiently acquire information in real time. The output, "Hybridview", is produced by fusing the information from digital camera and magnetic position sensors, and shows an overlay of the positions of organs and objects with the trajectory of instruments. An intelligent inference engine was designed to formulate an objective standard based on the expertise of senior surgeons and to provide an accurate scoring method. A multi-level fuzzy inference engine and new performance metrics were implemented.To demonstrate the feasibility of the proposed training system, numerous experiments were conducted. The results show that the situational awareness training system for MIS is useful and efficient.