Abstract
In this paper we propose a new methodology to model surgical procedures that is specifically tailored to semi-autonomous robotic surgery. We propose to use a restricted version of statecharts to merge the bottom-up approach, based on data-driven techniques (e.g., machine learning), with the top-down approach based on knowledge representation techniques. We consider medical knowledge about the procedure and sensing of the environment in two concurrent regions of the statecharts to facilitate re-usability and adaptability of the modules. Our approach allows producing a well defined procedural model exploiting the hierarchy capability of the statecharts, while machine learning modules act as soft sensors to trigger state transitions. Integrating data driven and prior knowledge techniques provides a robust, modular, flexible and re-configurable methodology to define a surgical procedure which is comprehensible by both humans and machines. We validate our approach on the three surgical phases of a Robot-Assisted Radical Prostatectomy (RARP) that directly involve the assistant surgeon: bladder mobilization, bladder neck transection, and vesicourethral anastomosis, all performed on synthetic manikins.
Highlights
The research interest in Robotic-assisted Minimally Invasive Surgery (R-MIS) is shifting from teleoperated devices to the development of autonomous support systems for the execution of repetitive surgical steps, such as suturing, ablation and microscopic image scanning
The concurrency capability of the statecharts will be exploited only to represent the parallelism of the sensing, while the hierarchy will be used only to separate the medical knowledge from the implementation of the robotic tasks
The standard set of rules for statecharts do not prevent the definition of edges over concurrent states, but in the case of the proposed procedure-observers statecharts this kind of transitions are meaningless as surgical procedures follow a well-defined sequence of states
Summary
The research interest in Robotic-assisted Minimally Invasive Surgery (R-MIS) is shifting from teleoperated devices to the development of autonomous support systems for the execution of repetitive surgical steps, such as suturing, ablation and microscopic image scanning. We adopt a safer approach that follows the engineering stack guidelines for which the top down model is adapted in its formulation to the events based on the available observations on both the environment and the robots [12]. The procedure region represents the medical knowledge extracted from clinical trials with surgeons and from literature review; the observer region is composed of a concurrent set of FSMs that provides a logical description for the environment state (e.g. semantic robot position, kinematics state, etc.).
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