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This paper presents an analysis of 67 minimally invasive surgical procedures covering 11 different procedure types to determine patterns of tool use. A new graph-theoretic approach was taken to organize and analyze the data. Through grouping surgeries by type, trends of common tool changes were identified. Using the concept of signal/noise ratio, these trends were found to be statistically strong. The tool-use trends were used to generate tool placement patterns for modular (multi-tool, cartridge-type) surgical tool systems, and the same 67 surgeries were numerically simulated to determine the optimality of these tool arrangements. The results indicate that aggregated tool-use data (by procedure type) can be employed to predict tool-use sequences with good accuracy, and also indicate the potential for artificial intelligence as a means of preoperative and/or intraoperative planning. Furthermore, this suggests that the use of multifunction surgical tools can be optimized to streamline surgical workflow

Tool Sequence Trends in Minimally Invasive Surgery: Statistical Analysis and Implications for Predictive Control of Multifunction Instruments