The Kinematics and the Dynamics of Minimally Invasive surgery - Objective Assessment of Surgical Performance Using Markov Models

 

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.

The Blue Dragon System (Overeview)

Blue DRAGON System

Blue DRAGON - Graphical User Interface (GUI)

Endoscopic Suturing - First year resident (R1)

 

Endoscopic Suturing - Expert Surgeon (E)

 

Markov Model

State Definitions (selected video clips)

The objective methodology for assessing skill while performing a procedure is inspired by the analogy between the human spoken language and surgery. Further analysis of this concept indicates that these two domains share similar taxonomy and internal etymological structure that allows a mathematical description of the process by using quantitative models. Such models can be further used to objectively assess skill level by revealing the internal structure and dynamics of the process. This analogy is enhanced by the fact that in both the human language and in surgery, an idea can be expressed and a procedure can be preformed in several different ways while retaining the same cognitive meaning or outcome. This fact suggests that a stochastic approach might describe the surgical or medical examination processes incorporating the inherent variability better then a determinist approach.

The critical step in creating such an analogy is to identify the prime elements. In the human language, the prime element is the ‘word’ which is analogous to a ‘tool/tissue interaction’  in surgery. This prime element is modeled by a ‘state’ in the model. As in a spoken language, words can have differently ‘pronunciations’ by various people and yet preserve their meaning. In surgery, various ‘force/torque magnitudes’ can be applied on the tissues and still be classified under the same tool/tissue interaction category. These various force/torque magnitudes are simulated by the ‘observations’ in the model. In a similar fashion to the human language in which a sequence of words are comprised into a sentence, and sentences create a ‘chapter’, a sequence to tool/tissue interactions form a step of an operation in which an intermediate and specific outcome can be completed. Each step of the operation is represented by a single model. ‘Multiple models’ can be further describing a multi-step ‘surgical operation’ that is analogous to a ‘story’. One may note that the sub-structures like a sentence and a section were omitted in the current analogy; however, identifying the corresponding elements in surgical procedure may increase the resolution of the model.

Analyzing the degrees of freedom (DOF) of a tool in MIS indicates that due to the introduction of the port through which the surgeon inserts tools into the body cavity, two DOF of the tool are restricted. The six DOF of a typical open surgical tool is reduced to only four DOF in a minimally invasive setup. These four DOF include rotation along the three orthogonal axes (x,y,z) and translation along the long axis of the tool’s shaft (z). A fifth DOF is defined as the tool-tip jaws angle, which is mechanically linked to the tool’s handle, when a grasper or a scissor is used. Additional one or two degrees of freedom can be obtained by adding a wrist joint to the MIS tool. The wrist joint has been incorporated into commercially available surgical robots in order to enhance the dexterity of the tool within the body cavity.

Surgeons, while performing MIS procedures, utilize various combinations of the tools’ DOF while manipulating them during the interaction with the tissues or other items in the surgical scene (needle, suture, staple etc.) in order to achieve the desired outcome. Quantitative analysis of the tool’s position and ordination during surgical procedures revealed 16 different combinations of the tool’s five DOF which will be further referred to, and modeled as states. The 16 states can be grouped into three types, based on the number of movements or DOF utilized simultaneously. The fundamental maneuvers are defined as Type I. The 'idle' state was defined as moving the tool in space (body cavity) without touching any internal organ, tissue, or any other item in the scene. The forces and torques developed in this state represent the interaction with the port and the abdominal wall, in addition to the gravitational and inertial forces. In the 'grasping' and 'spreading' states, compression and tension were applied on the tissue through the tool tip by closing and opening the grasper’s handle, respectively. In the 'pushing' state, the tissue was compressed by moving the tool along the Z axis. 'Sweeping' consisted of placing the tool in one position while rotating it around the X and/or Y axes or in any combination of these two axes (port frame). The rest of the tool/tissue interactions in Types II and III were combinations of the fundamental ones defined as Type I. The only one exception was state 15 that was observed only in tasks involved suturing when the surgeon grasps the needle and rotates it around the shaft’s long axis to insert it into the tissue. Such a rotation was never observed whenever direct tissue interaction was involved.

State- Idle

One Degree of Freedom (1 DOF)
State - Grasp	State - Push
State - Spread	State - Sweep
Two Degrees of Freedom (2 DOF)
State - Grasp - Pull	State - Grasp - Push
State - Grasp - Sweep	State - Push - Spread
State - Push Sweep	State - Sweep Spread
Three Degrees of Freedom (3 DOF)
State - Grasp Pull Sweep	State - Grasp Push Sweep
State - Push Sweep Spread

 

Markov Model (Expert Surgeon - Endoscopic Suturing) - Real Time
Matrices [A], [B], [C], are represented as color-coded probabilistic maps.

 

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Device

Blue DRAGON

| Status: Completed |

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PUblications

(*) Note: Most of the Bionics Lab publications are available on-line in a PDF format. You may used the publication's reference number as a link to the individual manuscript.

[ JP18] Rosen Jacob, Jeffrey D. Brown, Smita De, Mika N. Sinanan Blake Hannaford, Biomechanical Properties of Abdominal Organs In Vivo and Postmortem Under Compression Loads, ASME Journal of Biomedical Engineering, Vol. 130, Issue 2, April 2008

[ JP1] MacFarlane Mark, Jacob Rosen, Blake Hannaford, Carlos Pellegrini, Mika N. Sinanan, Force Feedback Grasper Helps Restore the Sense of Touch in Minimally Invasive Surgery, Journal of Gastrointestinal Surgery, Vol. 3, No. 3, pp. 278-285, May/June 1999.

[ JP2] Rosen Jacob, Blake Hannaford, Mark MacFarlane, Mika N. Sinanan, Force Controlled and Teleoperated Endoscopic Grasper for Minimally Invasive Surgery - Experimental Performance Evaluation, IEEE Transactions on Biomedical Engineering, Vol. 46, No. 10, pp. 1212-1221, October 1999.

[ CP2] Hannaford B., J. Trujillo, Mika N. Sinanan, M. Moreyra, Jacob Rosen, J. Brown, R. Lueschke, Mark MacFarlane, Computerized Endoscopic Surgical Grasper, Studies in Health Technology and Informatics - Medicine Meets Virtual Reality, Vol. 50, pp. 265-271, IOS Press, January 1998.

[ CP9] Brown Jeffrey D., Jacob Rosen, Manuel Moreyra, Mika N. Sinanan, Blake Hannaford, 'Computer-Controlled Motorized Endoscopic Grasper for In Vivo Measurements of Soft Tissue Biomechanical Characteristics,' Studies in Health Technology and Informatics - Medicine Meets Virtual Reality, vol. 85, pp. 71-73, IOS Press, January 2002.

[ CP12] Brown Jeffrey D., Jacob Rosen, Yoon Sang Kim, Lily Chang, Mika N. Sinanan, Blake Hannaford, In-Vivo and In-Situ Compressive Properties of Porcine Abdominal Soft Tissues, Studies in Health Technology and Informatics - Medicine Meets Virtual Reality, vol. 94, pp. 26-32, IOS Press, January 2003.

[ CP13] Brown Jeffrey D., Jacob Rosen, M. N. Sinanan, Blake Hannaford, In-Vivo and Postmortem Compressive Properties of Porcine Abdominal Organs, Lecture Notes in Computer Science, Volume 2878 / 2003, pp. 238 –245, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003, Toronto, Canada.

[ CP15] Brown Jeffrey D., Jacob Rosen, Lily Chang, Mika N. Sinanan, Blake Hannaford, Quantifying Surgeon Grasping Mechanics in Laparoscopy Using the Blue DRAGON System, Studies in Health Technology and Informatics - Medicine Meets Virtual Reality, vol. 98, pp. 34-36, IOS Press, January 2004