Generalized Approach for Modeling Minimally Invasive Surgery as a Stochastic Process Using a Discrete Markov Model

Abstract

Minimally invasive surgery (MIS) involves a multidimensional series of tasks requiring a synthesis between visual information and the kinematics and dynamics of the surgical tools. Analysis of these sources of information is a key step in defining objective criteria for characterizing surgical performance. The Blue DRAGON is a new system for acquiring the kinematics and the dynamics of two endoscopic tools synchronized with the endoscopic view of the surgical scene. Modeling the process of MIS using a finite state model [Markov model (MM)] reveals the internal structure of the surgical task and is utilized as one of the key steps in objectively assessing surgical performance. The experimental protocol includes tying an intracorporeal knot in a MIS setup performed on an animal model (pig) by 30 surgeons at different levels of training including expert surgeons. An objective learning curve was defined based on measuring quantitative statistical distance (similarity) between MM of experts and MM of residents at different levels of training. The objective learning curve was similar to that of the subjective performance analysis. The MM proved to be a powerful and compact mathematical model for decomposing a complex task such as laparoscopic suturing. Systems like surgical robots or virtual reality simulators in which the kinematics and the dynamics of the surgical tool are inherently measured may benefit from incorporation of the proposed methodology.

Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

“…Force-related metrics have been mostly validated for suturing tasks [49,50,56]. Additionally, Rosen et al demónstrate that experienced surgeons apply higher [56].…”

“…Another quality metricdetection of idle states-has not gained much attention. Nevertheless, studies show significant differences between a novice surgeon and a more experienced surgeon on suture tasks and more complex procedural chores [49,50]. The principie behind this metric is that a novice surgeon takes more time to plan the next move than a more experienced surgeon.…”

“…Typically, this assessment requires the presence of a trained supervisor, and the definition of clear, structured checklists are necessary [28,53]. Table 6 shows the most widespread HMT capture systems for surgical assessment identified in the literature [44][45][46][47][48][49][50][51][52][53][54][55][88][89][90][91]. Their classification can be done according to the nature of the acquired information.…”

“…Sequential analysis of surgical tasks by Markov modeling has been the most common approach [49,50,[106][107][108][109][110][111]. Simple Markov models (MM) interpret sequences of actions as a series of steps, defined by a closed number of states and the probabilities from going from one state to another.…”

“…States are usually taken as sets of predefined surgical maneuvers (or surgemes) [106]. MMs that decompose tasks into states based on force/torque patterns have been employed by Rosen et al for modeling surgical steps with an 86% success rate [50]. However, definition of these surgemes is often done manually and, as an alternative, hidden Markov models (HMM) have been widely used [49,[106][107][108][109][110][111].…”