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Quantifying Expert vs. Novice Skill In Vivo for Development of a Laryngoscopy Simulator Nathan J. Delson*, Ph.D., Nada Koussa*, Randolph H. Hastings**,

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Presentation on theme: "Quantifying Expert vs. Novice Skill In Vivo for Development of a Laryngoscopy Simulator Nathan J. Delson*, Ph.D., Nada Koussa*, Randolph H. Hastings**,"— Presentation transcript:

1 Quantifying Expert vs. Novice Skill In Vivo for Development of a Laryngoscopy Simulator Nathan J. Delson*, Ph.D., Nada Koussa*, Randolph H. Hastings**, M.D, Ph.D., Matthew B. Weinger**, M. D. University of California, San Diego - *Mechanical and Aerospace Engineering - **School of Medicine Veterans Administration San Diego Healthcare System

2 Laryngoscopy is used for Airway Intubation The failure to establish the airway is a major cause of death and brain injury Laryngoscopy is the most common technique for establishing an airway. A laryngoscope is used to visualize the glottic opening to the lungs, and then an endotracheal tube is inserted into the trachea. Experience makes the difference Experts succeed 99.9% Novices succeed 67% to 90%

3 Project Objectives Long Term Develop realistic training simulator Provide guidance to trainee Quantitative assessment of trainee Short Term Quantify expert skill and novice errors Measure relevant physical properties of airway in vivo

4 Expert Skill Acquisition Instrument tools to measure: Sensory information used by expert Actions of expert Collect data under a range of environments Range of patient anatomy Quantify characteristics of expert actions and novice errors Identify physical properties of environment where interaction occurs

5 Instrumented Laryngoscope 6 axis force/torque sensor (ATI) 6 dof magnetic position sensor (Ascension) DisassembledAssembled

6 Data Collection in the OR Position Sensors Skull Adam ’ s Apple Jaw Protocol Patients undergoing Anesthesia Expert and novice on same patient Use stylus to gather profile and calibration points Grade of view reported post procedure

7 Animation of Expert Side View Laryngoscope in blue Force vector in red Click figure for animation

8 Data Interpretation is Preliminary Variation between patients is large Some novices performed well Prior experience Easy patient anatomy Number of procedures is low Data is presented as indication of method potential rather than definitive characterization of task

9 Laryngoscope placement seems more important than head position (for this case!) Laryngoscope final position at maximum force for both expert and novice is very similar relative to the patient’s head anatomy The expert tilted the head much more forward than novice. However, they both acquired the same grade view Expert in blue, Novice in red at maximum force

10 Incorrect laryngoscope placement reduces grade view of novice Expert in blue, Novice in red at maximum force

11 Expert vs. Novice in Head c.s. Novice blade placement is too far into throat VA_AB 17 NoviceExpert Max Force 75.2 N43.8 N Laryn Pitch at Max Y -75.9 o -60.2 o Distance Traveled 126 cm51 cm View Attained Grade IIGrade I

12 Expert vs. Novice in Head c.s. VA_AB 12 NoviceExpert Max Force 24.2 N24.9 N Laryn Pitch at Max Y -28.9 o -26.0 o Distance Traveled 42 cm75 cm View Attained Grade IIIGrade I Novice blade placement is not far enough into throat

13 Expert vs. Expert in Head c.s. VA_AB 10 Expert Max Force 34.9 N35.4 N Laryn Pitch at Max Y -80.0 o -70.4 o Distance Traveled 52 cm84 cm View Attained Grade I Both experts have similar blade placement and trajectory

14 Expert vs. Expert in Head c.s. VA_AB 16 Expert Max Force 38.6 N38.9 N Laryn Pitch at Max Y -85.7 o -70.5 o Distance Traveled 85 cm119 cm View Attained Grade I Both experts have similar blade placement but vary trajectory at points

15 Expert vs Novice Comments When novice blade placement differs from expert, grade view can be lower Experts have similar blade placement locations on the same patient, although trajectories can vary in certain regions  regions of expertise can be defined

16 Region of Expertise can be Used to Identify Novice Error Application of Robot Programming by Human Demonstration Obstacle avoidance example Deviation from expert region would indicate error, allowing for real time guidance cues during training simulation

17 Estimation of Physical Properties In Vivo Advantages of Skill Acquisition Approach Contact forces are at ranges of interest and at locations of interest Challenges Unlike probing, motion is not defined for tissue characterization Both sliding and compression occur Sampling occurs at just a few tissue locations Tissue swelling may occur between procedures

18 Stiffness Estimation Method Stiffness is approximated as single dimensional Stiffness is estimated during final loading and unloading when sliding is minimal

19 Stiffness calculation in head coordinate system estimates compliance of airway tissue δF  - K δX K load  41.93/ 50.3 =.833 N/mm K unload  32.28 /84.7 = 3.81 N/mm Unloading occurs rapidly with smaller displacement, resulting in higher stiffness

20 Stiffness calculation in table coordinate system estimates overall head and neck resistance δF  - K δX K load  41.93/ 64.1 =.655 N/mm K unload  32.28 /125.6 =2.57N/mm Stiffness is lower in table c.s. since motion includes head displacement

21 Stiffness Calculations in Same Patient for both Expert and Novice Here expert placed blade less deep into the throat and attained same view as novice with lower force

22 Effective stiffness of expert is lower Expert loading stiffness =.636 N/mm Novice loading stiffness = 1.29 N/mm Expert blade placement may be in “sweet spot” where stiffness is lower Tissue swelling between procedures may also be a factor

23 Comments on Tissue Properties Preliminary stiffness properties have been identified High nonlinearity observed in unloading Realistic simulator should mimic resistance to force (impedance) due to both tissue compliance and head/neck displacement Separating sliding forces from compressive forces may allow for stiffness estimations at more regions

24 Future Direction Comparison of stiffness properties to existing mannequins Add image capture Collect more expert vs expert data

25 Acknowledgments Talla Farivar, Lance Feller, Anjali Godbole, William Green, Andrew Linn, and Nabyl Tejani Society for Technology in Anesthesia Anesthesia Patient Safety Foundation


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